Add mcp-agent cloud compatibility to financial analysis agent example #499
Conversation
WalkthroughRefactors the MCP Financial Analyzer into an orchestrated AugmentedLLM workflow: adds an @app.tool Changes
Sequence Diagram(s)sequenceDiagram
autonumber
actor User
participant CLI as CLI (main)
participant App as MCPApp (unified_stock_analyzer)
participant Tool as analyze_stock
participant Orch as Orchestrator (full)
participant C as EvaluatorOptimizerLLM (research_quality_controller)
participant R as research_agent (search_finder)
participant E as research_evaluator
participant A as analyst_agent (financial_analyst)
participant W as report_writer
User->>CLI: provide company name
CLI->>App: app.run(analyze_stock, company)
App->>Tool: invoke
Tool->>Orch: run(task)
Orch->>C: request quality-controlled research
C->>R: gather data (server_names=["fetch"])
R-->>C: research results
C->>E: evaluate results
E-->>C: rating + feedback
alt rating < EXCELLENT
C->>R: iterate with feedback
R-->>C: improved results
C->>E: re-evaluate
E-->>C: rating
end
C-->>Orch: verified research
Orch->>A: produce analysis
A-->>Orch: analysis
Orch->>W: write formatted report
W-->>Orch: final report text
Orch-->>Tool: report text
Tool-->>App: return string
App-->>CLI: result
CLI-->>User: print report
Estimated code review effort🎯 3 (Moderate) | ⏱️ ~25 minutes Possibly related PRs
Suggested reviewers
Poem
Pre-merge checks and finishing touches✅ Passed checks (3 passed)
✨ Finishing touches🧪 Generate unit tests
Tip 👮 Agentic pre-merge checks are now available in preview!Pro plan users can now enable pre-merge checks in their settings to enforce checklists before merging PRs.
Please see the documentation for more information. Example: reviews:
pre_merge_checks:
custom_checks:
- name: "Undocumented Breaking Changes"
mode: "warning"
instructions: |
Pass/fail criteria: All breaking changes to public APIs, CLI flags, environment variables, configuration keys, database schemas, or HTTP/GraphQL endpoints must be documented in the "Breaking Change" section of the PR description and in CHANGELOG.md. Exclude purely internal or private changes (e.g., code not exported from package entry points or explicitly marked as internal).Please share your feedback with us on this Discord post. Thanks for using CodeRabbit! It's free for OSS, and your support helps us grow. If you like it, consider giving us a shout-out. Comment |
![coderabbitai[bot]](https://avatars.githubusercontent.com/in/347564?s=60&v=4){kind=small}
There was a problem hiding this comment.
Actionable comments posted: 3
🧹 Nitpick comments (7)
examples/usecases/mcp_financial_analyzer/main.py (7)
409-411: Hard-coded model; parameterize for cloud deploymentsLet operators set the model via environment variable.
- result = await orchestrator.generate_str( - message=task, request_params=RequestParams(model="gpt-4o") - ) + result = await orchestrator.generate_str( + message=task, + request_params=RequestParams(model=os.getenv("OPENAI_MODEL", "gpt-4o")) + )
255-256: EST is wrong half the year; make the report timestamp timezone-awareUse zoneinfo to render ET correctly (EDT/EST).
- **Report Date:** {datetime.now().strftime('%B %d, %Y at %I:%M %p EST')} + **Report Date:** {datetime.now(ZoneInfo("America/New_York")).strftime('%B %d, %Y at %I:%M %p %Z')}Add import (see next comment).
8-17: Missing import for timezone fixAdd
ZoneInfoimport.from datetime import datetime +from zoneinfo import ZoneInfo
34-39: Dead path prep and hidden dependency on output_path inside f-string
- You compute
timestamp/output_pathbut don’t write files.- The writer prompt still interpolates
{output_path}inside an f-string (even as a “comment”), forcing that variable to exist unnecessarily.Either drop the prompt line or make it literal, then remove the unused variables.
- timestamp = datetime.now().strftime("%Y%m%d_%H%M%S") - # output_file = f"{company_name.lower().replace(' ', '_')}_report_{timestamp}.md" - output_path = os.path.join(OUTPUT_DIR, f"{company_name.lower().replace(' ', '_')}_report_{timestamp}.md") + # Note: file output disabled; results are returned/printed. @@ - # - Save to file: {output_path} (COMMENTED OUT - NOW PRINTING RESULTS) + # (File saving disabled in this example; results are returned as text.)And remove unused globals:
-OUTPUT_DIR = "company_reports" -COMPANY_NAME = "Apple" if len(sys.argv) <= 1 else sys.argv[1] +# File output disabled; no OUTPUT_DIR needed.Also applies to: 363-365
22-26: MAX_ITERATIONS defined but unusedWire it into
EvaluatorOptimizerLLM(max_refinements=MAX_ITERATIONS)(see earlier diff), or remove it.
372-385: Optional: give the Orchestrator a name for clearer logs/telemetryNot required, but helps tracing in cloud runs.
- orchestrator = Orchestrator( + orchestrator = Orchestrator( + name="stock_orchestrator", llm_factory=OpenAIAugmentedLLM,
54-115: Prompt hygiene: tighten “exact numbers” requirement to avoid hallucinated precisionGiven you rely on
fetch, instruct agents to quote figures verbatim with source/date and to state “not found” instead of fabricating. Consider adding “do not infer or round; copy from source” to each agent’s instruction.If helpful, I can propose a compact set of shared prompt rules to include across agents.
Also applies to: 118-183, 194-245, 248-370
📜 Review details
Configuration used: CodeRabbit UI
Review profile: CHILL
Plan: Pro
📒 Files selected for processing (2)
examples/usecases/mcp_financial_analyzer/main.py(2 hunks)examples/usecases/mcp_financial_analyzer/mcp_agent.config.yaml(0 hunks)
💤 Files with no reviewable changes (1)
- examples/usecases/mcp_financial_analyzer/mcp_agent.config.yaml
🧰 Additional context used
🧬 Code graph analysis (1)
examples/usecases/mcp_financial_analyzer/main.py (4)
src/mcp_agent/app.py (2)
tool(717-769)run(397-420)src/mcp_agent/agents/agent.py (1)
Agent(62-1162)src/mcp_agent/workflows/evaluator_optimizer/evaluator_optimizer.py (2)
EvaluatorOptimizerLLM(49-478)QualityRating(25-31)src/mcp_agent/workflows/orchestrator/orchestrator.py (1)
Orchestrator(116-716)
🔇 Additional comments (1)
examples/usecases/mcp_financial_analyzer/main.py (1)
388-405: Task text uses controller by name; ensure it matches the componentOnce you set
name="research_quality_controller", this aligns. Without that, the orchestrator can’t resolve the agent.
| research_quality_controller = EvaluatorOptimizerLLM( | ||
| optimizer=research_agent, | ||
| evaluator=research_evaluator, | ||
| llm_factory=OpenAIAugmentedLLM, | ||
| min_rating=QualityRating.EXCELLENT, | ||
| ) |
There was a problem hiding this comment.
Orchestrator name resolution: give the controller a stable name and wire max refinements
Your task text later refers to research_quality_controller by name, but EvaluatorOptimizerLLM here has no name. Orchestrator resolves by agent.name; this mismatch can break planning/execution. Also, you defined MAX_ITERATIONS but didn’t pass it.
research_quality_controller = EvaluatorOptimizerLLM(
- optimizer=research_agent,
- evaluator=research_evaluator,
- llm_factory=OpenAIAugmentedLLM,
- min_rating=QualityRating.EXCELLENT,
+ optimizer=research_agent,
+ evaluator=research_evaluator,
+ llm_factory=OpenAIAugmentedLLM,
+ min_rating=QualityRating.EXCELLENT,
+ max_refinements=MAX_ITERATIONS,
+ name="research_quality_controller",
)📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| research_quality_controller = EvaluatorOptimizerLLM( | |
| optimizer=research_agent, | |
| evaluator=research_evaluator, | |
| llm_factory=OpenAIAugmentedLLM, | |
| min_rating=QualityRating.EXCELLENT, | |
| ) | |
| research_quality_controller = EvaluatorOptimizerLLM( | |
| optimizer=research_agent, | |
| evaluator=research_evaluator, | |
| llm_factory=OpenAIAugmentedLLM, | |
| min_rating=QualityRating.EXCELLENT, | |
| max_refinements=MAX_ITERATIONS, | |
| name="research_quality_controller", | |
| ) |
🤖 Prompt for AI Agents
In examples/usecases/mcp_financial_analyzer/main.py around lines 186 to 191, the
EvaluatorOptimizerLLM instance is missing a stable name and the configured
MAX_ITERATIONS value; set the orchestrator name to "research_quality_controller"
via the name parameter and pass the MAX_ITERATIONS constant (or a properly named
max_refinements/max_iterations argument expected by EvaluatorOptimizerLLM) into
the constructor so the orchestrator can be resolved by agent.name and the max
refinement iterations are honored.
There was a problem hiding this comment.
Actionable comments posted: 1
Caution
Some comments are outside the diff and can’t be posted inline due to platform limitations.
⚠️ Outside diff range comments (1)
examples/usecases/mcp_financial_analyzer/README.md (1)
1-1: Doc inconsistency: g-search vs fetch-only cloud flowThe title and earlier setup still position this as “with Google Search” and instruct installing
g-search-mcp, but the new cloud section states the agent “uses only the fetch server.” Please reconcile: either split “Local (g-search MCP)” vs “Cloud (fetch)” explicitly, or remove the g-search path if deprecated. Also consider renaming the title to drop “with Google Search.”Example adjustments (outside changed block):
-# MCP Financial Analyzer with Google Search +# MCP Financial Analyzer (Local and Cloud) @@ -Install the g-search-mcp server (from https://github.com/jae-jae/g-search-mcp): +Optional (Local mode only): Install the g-search MCP server (https://github.com/jae-jae/g-search-mcp).Also applies to: 65-69, 103-104
🧹 Nitpick comments (4)
examples/usecases/mcp_financial_analyzer/README.md (4)
119-126: Markdown lint fixes: trailing punctuation in heading and missing language for fenced block
- Remove trailing “:” in the heading (MD026).
- Add a language to the following fenced block (use “text”) (MD040).
-#### For OpenAI API Key: +#### For OpenAI API Key-``` +```text Select secret type for 'openai.api_key'--- `136-155`: **SSE config looks good; add a minor hardening note** Config is clear and uses an env var for the bearer token. Consider adding a one-liner reminding users to scope/rotate the token and avoid committing this file. ```diff "env": { "BEARER_TOKEN": "your-mcp-agent-cloud-api-token" } } } } +// Tip: Use a minimally-scoped token, rotate regularly, and do not commit real tokens to VCS.
176-184: Clarify data source to match “fetch-only” claimThis section says “using web search,” but prerequisites say “uses only the fetch server.” Align to avoid confusion.
-– Conducts comprehensive financial research using web search +– Conducts comprehensive financial research using the MCP fetch server
189-191: Add language to fenced prompt block (MD040)Specify “text” to satisfy markdownlint and improve rendering.
-``` +```text Please analyze Tesla's financial performance and investment outlook.</blockquote></details> </blockquote></details> <details> <summary>📜 Review details</summary> **Configuration used**: CodeRabbit UI **Review profile**: CHILL **Plan**: Pro <details> <summary>📥 Commits</summary> Reviewing files that changed from the base of the PR and between c35f5a21ed470e968ba5a308cfa382a37e364c06 and 790722d0292bbc61936cc963edd972f29652f5ce. </details> <details> <summary>📒 Files selected for processing (1)</summary> * `examples/usecases/mcp_financial_analyzer/README.md` (1 hunks) </details> <details> <summary>🧰 Additional context used</summary> <details> <summary>🪛 markdownlint-cli2 (0.17.2)</summary> <details> <summary>examples/usecases/mcp_financial_analyzer/README.md</summary> 119-119: Trailing punctuation in heading Punctuation: ':' (MD026, no-trailing-punctuation) --- 120-120: Fenced code blocks should have a language specified (MD040, fenced-code-language) --- 189-189: Fenced code blocks should have a language specified (MD040, fenced-code-language) </details> </details> </details> </details> <!-- This is an auto-generated comment by CodeRabbit for review status -->
| ## `4` [Beta] Deploy to MCP Agent Cloud | ||
|
|
||
| ### Prerequisites | ||
| This agent is already cloud-compatible with the `@app.tool` decorator and uses only the `fetch` server for web data collection. | ||
|
|
||
| ### Step 1: Login to MCP Agent Cloud | ||
|
|
||
| ```bash | ||
| uv run mcp-agent login | ||
| ``` | ||
|
|
||
| ### Step 2: Deploy your agent | ||
|
|
||
| ```bash | ||
| uv run mcp-agent deploy financial-analyzer | ||
| ``` | ||
|
|
There was a problem hiding this comment.
🧩 Analysis chain
CLI commands may need verification
“uv run mcp-agent login|deploy …” looks plausible, but CLI subcommands/entrypoint names are brittle. Please confirm exact command names and flags.
🌐 Web query:
What are the current mcp-agent CLI subcommands for authentication and deployment, and example invocations (including SSE URL usage) as of September 2025?
💡 Result:
Short answer — common CLI subcommands (Sep 2025) and example invocations:
- agentauth-mcp (proxy for connecting MCP clients to remote MCP servers)
- Subcommands: generate, connect.
- Examples:
- Generate local test credentials:
agentauth-mcp generate. [1] - Connect an MCP client to a remote MCP URL with a custom header:
agentauth-mcp connect https://api.example.com/mcp --header "API-Key:${API_KEY}". - Connect using AgentAuth token (set AGENTAUTH_TOKEN in env):
AGENTAUTH_TOKEN=aa-... agentauth-mcp connect https://api.example.com/mcp. [1]
- Generate local test credentials:
- Using an SSE MCP server URL directly from an MCP client / npx command (common pattern used by mcp-agent and related projects)
- No special mcp-agent “auth” subcommand — you configure the MCP server entry to use an SSE URL (and headers) or run an npx wrapper that supplies the SSE URL.
- Examples (mcp_agent.config.yaml / npx style):
- npx/server invocation embedding SSE URL:
command: "npx"
args: ["-y","@modelcontextprotocol/server-sse","https://localhost:8000/sse"] - or with token header in config:
mcp:
servers:
sse_server:
transport: "sse"
url: "https://localhost:8000/sse"
headers:
Authorization: "Bearer your-token" - Inline example used by some projects (Agent.ai style):
args: ["-y","@modelcontextprotocol/server-sse","https://mcp.example.com/YOUR_API_TOKEN_HERE/sse"]. [2][3]
- npx/server invocation embedding SSE URL:
- mcp-agent (lastmile-ai) deployment / run commands (common usage patterns)
- Install and run examples / deploy agent:
- Install: pip install mcp-agent (or uv add "mcp-agent" / uv run main.py for example apps).
- Run example app locally:
uv run main.py - Expose an agent as an MCP server by configuring mcp_agent.config.yaml (see SSE/WebSocket/http transports above) and run the app to serve it. [2][3]
Notes / sources
- agentauth-mcp (CLI: generate, connect; header support examples). [1]
- mcp-agent docs (config examples showing SSE / headers / transports). [3]
- Agent.ai / similar projects showing embedding SSE URL into npx args (common invocation pattern). [2]
If you want, I can produce exact copy-paste-ready CLI snippets and a sample mcp_agent.config.yaml that shows auth headers + SSE URL for your environment.
Replace inaccurate CLI examples with agentauth-mcp / SSE-config usage
File: examples/usecases/mcp_financial_analyzer/README.md (lines 100–116) — the shown commands uv run mcp-agent login and uv run mcp-agent deploy … are not standard. Replace with one of the common, copy-paste patterns below:
-
Auth (replace
login):- agentauth-mcp connect https://mcp.example.com/sse --header "Authorization: Bearer ${MCP_TOKEN}"
- or: AGENTAUTH_TOKEN=aa-... agentauth-mcp connect https://mcp.example.com/sse
-
SSE config (preferred inline example instead of a “login”): add in mcp_agent.config.yaml:
mcp:
servers:
sse_server:
transport: "sse"
url: "https://localhost:8000/sse"
headers:
Authorization: "Bearer your-token" -
Run/deploy (replace
deploy): show install + run or service config, e.g.:
pip install mcp-agent
uv run main.py
—or— explain how to expose the agent using the mcp_agent.config.yaml + run command.
🤖 Prompt for AI Agents
In examples/usecases/mcp_financial_analyzer/README.md around lines 100–116 the
README shows nonstandard CLI commands `uv run mcp-agent login` and `uv run
mcp-agent deploy …`; replace them with the suggested agentauth-mcp / SSE-config
patterns: remove the incorrect `uv run mcp-agent` examples, add an auth example
using either `agentauth-mcp connect https://mcp.example.com/sse --header
"Authorization: Bearer ${MCP_TOKEN}"` or an environment variant
`AGENTAUTH_TOKEN=aa-... agentauth-mcp connect https://mcp.example.com/sse`,
include the preferred inline mcp_agent.config.yaml SSE snippet (mcp -> servers
-> sse_server with transport/url/headers Authorization) and replace the `deploy`
example with explicit install + run instructions (e.g., pip install mcp-agent
and uv run main.py) or explain how to start the agent using the provided
mcp_agent.config.yaml so readers have a copy-pasteable auth and run workflow.
![graphite-app[bot]](https://avatars.githubusercontent.com/in/158384?s=60&v=4){kind=small}
| The agent will automatically: | ||
| 1. Research Tesla's current stock price, earnings, and recent news |
There was a problem hiding this comment.
There's an inconsistency in the example usage section. In line 184, the example shows analyzing Meta:
Please analyze Meta's financial performance and investment outlook.
But in line 188, it states the agent will research Tesla:
1. Research Tesla's current stock price, earnings, and recent news
The company name should be consistent between these lines - either both should reference Meta or both should reference Tesla.
| The agent will automatically: | |
| 1. Research Tesla's current stock price, earnings, and recent news | |
| The agent will automatically: | |
| 1. Research Meta's current stock price, earnings, and recent news | |
Spotted by Diamond
Is this helpful? React 👍 or 👎 to let us know.
There was a problem hiding this comment.
Actionable comments posted: 4
📜 Review details
Configuration used: CodeRabbit UI
Review profile: CHILL
Plan: Pro
📒 Files selected for processing (1)
examples/usecases/mcp_financial_analyzer/README.md(1 hunks)
🧰 Additional context used
🧠 Learnings (1)
📚 Learning: 2025-07-22T18:59:49.368Z
Learnt from: CR
PR: lastmile-ai/mcp-agent#0
File: examples/usecases/reliable_conversation/CLAUDE.md:0-0
Timestamp: 2025-07-22T18:59:49.368Z
Learning: Applies to examples/usecases/reliable_conversation/examples/reliable_conversation/src/**/*.py : Use mcp-agent's Agent abstraction for ALL LLM interactions, including quality evaluation, to ensure consistent tool access, logging, and error handling.
Applied to files:
examples/usecases/mcp_financial_analyzer/README.md
🪛 markdownlint-cli2 (0.18.1)
examples/usecases/mcp_financial_analyzer/README.md
114-114: Fenced code blocks should have a language specified
(MD040, fenced-code-language)
183-183: Fenced code blocks should have a language specified
(MD040, fenced-code-language)
🔇 Additional comments (2)
examples/usecases/mcp_financial_analyzer/README.md (2)
101-109: Replace nonstandard CLI: “uv run mcp-agent login/deploy” are likely incorrectUse AgentAuth or SSE config patterns instead (keep it copy‑pasteable). See prior reviewer’s note; please update accordingly.
Proposed replacements:
# Step 1: Authenticate/connect (example) agentauth-mcp connect https://[your-agent-server-id].deployments.mcp-agent.com/sse --header "Authorization: Bearer ${MCP_TOKEN}" # Or with env: AGENTAUTH_TOKEN=aa-... agentauth-mcp connect https://[your-agent-server-id].deployments.mcp-agent.com/sseOptionally add the SSE server config in your MCP client:
mcp: servers: cloud_agent: transport: sse url: https://[your-agent-server-id].deployments.mcp-agent.com/sse headers: Authorization: Bearer ${MCP_TOKEN}To confirm the correct CLI and flags, run this web query:
What are the current mcp-agent CLI subcommands for authentication and deployment, and example invocations (including SSE URL usage) as of September 2025?
132-149: Replace mcp-remote with the official @modelcontextprotocol/server-sse (security advisory)mcp-remote is a community CLI/proxy and has a 2025 OS command-injection advisory — switch to npx @modelcontextprotocol/server-sse, or if you must keep mcp-remote, pin, audit, and document mitigations.
File: examples/usecases/mcp_financial_analyzer/README.md lines 132-149
"financial-analyzer": { "command": "/path/to/npx", "args": [ - "mcp-remote", - "https://[your-agent-server-id].deployments.mcp-agent.com/sse", - "--header", - "Authorization: Bearer ${BEARER_TOKEN}" + "-y", + "@modelcontextprotocol/server-sse", + "https://[your-agent-server-id].deployments.mcp-agent.com/sse", + "--header", + "Authorization: Bearer ${BEARER_TOKEN}" ], "env": { "BEARER_TOKEN": "your-mcp-agent-cloud-api-token" } }
| ``` | ||
| Select secret type for 'openai.api_key' | ||
| 1: Deployment Secret: The secret value will be stored securely and accessible to the deployed application runtime. | ||
| 2: User Secret: No secret value will be stored. The 'configure' command must be used to create a configured application with this secret. | ||
| ``` |
There was a problem hiding this comment.
🧹 Nitpick
Add language to fenced code block (markdownlint MD040)
Specify a language for linting/renderers.
-```
+```text
Select secret type for 'openai.api_key'🧰 Tools
🪛 markdownlint-cli2 (0.18.1)
114-114: Fenced code blocks should have a language specified
(MD040, fenced-code-language)
🤖 Prompt for AI Agents
examples/usecases/mcp_financial_analyzer/README.md around lines 114 to 118, the
fenced code block lacks a language specifier which triggers markdownlint MD040;
update the opening fence to include a language token (for example "text" or
"console") so it becomes ```text (or ```console) and leave the block contents
unchanged to satisfy linters and improve renderer behavior.
| ### Available Tools | ||
|
|
||
| Once deployed, your agent will expose the `analyze_stock` tool, which: | ||
| - Takes a company name as input (e.g., "Apple", "Microsoft") | ||
| - Conducts comprehensive financial research using web search | ||
| - Performs quality evaluation and improvement loops to ensure data accuracy | ||
| - Generates professional investment analysis with bull/bear cases | ||
| - Returns a complete financial report as formatted text | ||
|
|
There was a problem hiding this comment.
🧹 Nitpick
Align wording: “web search” → “MCP fetch”
Prerequisites state only the fetch server is used. Adjust for consistency.
- - Conducts comprehensive financial research using web search
+ - Conducts comprehensive financial research using the MCP fetch server (HTTP retrieval)📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| ### Available Tools | |
| Once deployed, your agent will expose the `analyze_stock` tool, which: | |
| - Takes a company name as input (e.g., "Apple", "Microsoft") | |
| - Conducts comprehensive financial research using web search | |
| - Performs quality evaluation and improvement loops to ensure data accuracy | |
| - Generates professional investment analysis with bull/bear cases | |
| - Returns a complete financial report as formatted text | |
| ### Available Tools | |
| Once deployed, your agent will expose the `analyze_stock` tool, which: | |
| - Takes a company name as input (e.g., "Apple", "Microsoft") | |
| - Conducts comprehensive financial research using the MCP fetch server (HTTP retrieval) | |
| - Performs quality evaluation and improvement loops to ensure data accuracy | |
| - Generates professional investment analysis with bull/bear cases | |
| - Returns a complete financial report as formatted text | |
🤖 Prompt for AI Agents
In examples/usecases/mcp_financial_analyzer/README.md around lines 170 to 178,
the tool description incorrectly says it "Conducts comprehensive financial
research using web search" while the prerequisites and architecture use the MCP
fetch server; change the phrase "web search" to "MCP fetch" (or "MCP fetch
server") for consistency and, if there is a prerequisites section elsewhere,
ensure it explicitly mentions the MCP fetch server as the data source so wording
is consistent across the README.
| ``` | ||
| Please analyze Meta's financial performance and investment outlook. | ||
| ``` | ||
|
|
There was a problem hiding this comment.
🧹 Nitpick
Add language to fenced code block (markdownlint MD040)
Mark the example as text.
-```
+```text
Please analyze Meta's financial performance and investment outlook.🧰 Tools
🪛 markdownlint-cli2 (0.18.1)
183-183: Fenced code blocks should have a language specified
(MD040, fenced-code-language)
🤖 Prompt for AI Agents
In examples/usecases/mcp_financial_analyzer/README.md around lines 183 to 186
the fenced code block lacks a language specifier causing markdownlint MD040;
update the fence to include a language (e.g., "text") so it reads ```text before
the content and ``` after to mark the example as plain text.
| The agent will automatically: | ||
| 1. Research Tesla's current stock price, earnings, and recent news | ||
| 2. Evaluate data quality and improve if needed | ||
| 3. Analyze the financial data for investment insights | ||
| 4. Generate a comprehensive report with recommendations |
There was a problem hiding this comment.
🧹 Nitpick
Fix entity mismatch: Tesla vs Meta
The narrative should match the Meta prompt above.
-1. Research Tesla's current stock price, earnings, and recent news
+1. Research Meta's current stock price, earnings, and recent news📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| The agent will automatically: | |
| 1. Research Tesla's current stock price, earnings, and recent news | |
| 2. Evaluate data quality and improve if needed | |
| 3. Analyze the financial data for investment insights | |
| 4. Generate a comprehensive report with recommendations | |
| The agent will automatically: | |
| 1. Research Meta's current stock price, earnings, and recent news | |
| 2. Evaluate data quality and improve if needed | |
| 3. Analyze the financial data for investment insights | |
| 4. Generate a comprehensive report with recommendations |
🤖 Prompt for AI Agents
In examples/usecases/mcp_financial_analyzer/README.md around lines 187 to 191,
the step list references "Tesla" but must match the Meta prompt; replace "Tesla"
with "Meta" and ensure any related phrasing (e.g., stock price, earnings, recent
news) consistently refers to Meta across the four bullets so the narrative
matches the prompt above.
Summary by CodeRabbit
New Features
Changes
Documentation
Chores