equity-intel-mcp

Server Quality Checklist

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Latest release: v0.1.0
Disambiguation5/5
Each tool targets a distinct data source: analyst consensus, comprehensive analysis, price quote, insider activity, options signals, superinvestor holdings, and valuation. There is no overlap in purpose.
Naming Consistency4/5
All tools start with 'equity_' and use snake_case, but the verb/noun order varies (e.g., 'analyze_ticker' vs. 'analyst_consensus' vs. 'superinvestors'). Still, the pattern is clear and predictable.
Tool Count5/5
Seven tools provide a focused yet comprehensive set for equity analysis, covering key signals (price, valuation, sentiment, insider activity). The number is appropriate for the domain.
Completeness4/5
The tools cover major analysis dimensions: fundamental valuation, price context, analyst consensus, insider activity, options flow, and superinvestor holdings. Minor gaps like technical analysis or news sentiment are absent but not critical for the stated purpose.
Average 4.4/5 across 7 of 7 tools scored. Lowest: 3.8/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 7 commits in the last 12 weeks
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI status not available
This repository is licensed under MIT License.
This repository includes a README.md file.
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How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations indicate readOnlyHint=true and idempotentHint=true. The description adds useful behavioral context: the scoring logic (positive/negative), the effect of a fragile balance sheet halving positive scores, and the output structure. No contradictions with annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured with sections, concisely explains the methodology, and provides examples. Every sentence contributes useful information. Slightly verbose on the 'Args' section which duplicates schema info, but overall efficient.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (single parameter with two fields, output schema exists), the description is fairly complete: it explains the return values, scoring, and examples. However, it lacks latency or data freshness info, which would be helpful for an agent planning calls.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is reported as 0%, but the actual schema does contain descriptions for parameters. However, the description text only restates the parameter names and types without adding any new meaning beyond the schema. The description does not compensate for the low coverage by explaining the significance of parameters or their usage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool estimates fair value and financial health for a stock, using specific methodology (forward EPS × sector P/E band). It also names the data source (Yahoo Finance). This distinguishes it from sibling tools like equity_get_quote or equity_analyst_consensus.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description outlines the tool's function and provides examples, but does not explicitly state when to use this tool over alternatives like equity_get_quote for current price or equity_analyst_consensus for analyst targets. Usage context is implied but not directly compared.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, destructiveHint. Description adds that data comes from yfinance and warns about noise in options flow, enriching the behavioral understanding beyond annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Well-structured with a summary, explanatory paragraph, and Args/Returns/Examples. Front-loaded with main purpose. Slightly verbose but each part adds value.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity and presence of output schema, description covers input, output fields, and behavioral caveats. No significant gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, but description compensates by listing Args with ticker and response_format, giving examples (e.g., 'NVDA', 'AAPL'), and explaining output format. This adds value over the schema's basic descriptions.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states 'Get the 1-month implied move and put/call OI skew for a stock' with source (yfinance). It uses specific verbs and resource, and distinguishes from sibling tools like equity_analyze_ticker by focusing on options signals.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Description provides usage context: implied move as risk-sizing tool, and directional score as low conviction secondary indicator. It implies when to use (when needing options market sentiment) but does not explicitly contrast with sibling tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Discloses aggregation of P and S transactions, directional score range -100 to +100, env var requirement, and 'no signal' case. Annotations already indicate read-only, idempotent, non-destructive; description adds valuable behavioral context without contradiction.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Well-structured with clear sections. Each sentence is informative, though slightly verbose. Could be tightened but overall effective.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Covers the score calculation, edge case (no signal), env var requirement, and return format. Combined with schema and output schema, provides complete guidance for agent usage.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Input schema already provides detailed descriptions for 'ticker' and 'response_format' (100% coverage). The description merely restates parameter names and types, adding no additional semantic value beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
Clearly states the tool measures net insider buying vs selling from SEC Form 4 filings over 180 days. The verb 'measure' and resource 'SEC Form 4 filings' are specific, and the scope distinguishes it from sibling tools like equity_analyst_consensus.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides context that insider buying is a predictive signal and specifies behavior when no data ('no signal'). However, it does not explicitly compare to alternative tools or state when not to use.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate read-only, idempotent, and non-destructive. The description adds valuable behavioral context: 'Informational (not a buy/sell signal)' and explains the 52-week range calculation. No contradiction with annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured, front-loaded with purpose, followed by an informational note, args, returns, and examples. Every sentence adds value, and there is no redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity and the presence of output schema (implied by detailed return field listing), the description fully covers what the agent needs: purpose, parameters, return format, and usage examples. Annotations provide safety signals.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description explains both parameters (ticker and response_format) with examples, adding meaning beyond the schema. For instance, it clarifies that response_format can be 'markdown' or 'json' and provides example queries.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool gets a current price snapshot with 52-week range context, using specific verbs and resource. It distinguishes itself from sibling tools like equity_analyst_consensus or equity_valuation by focusing solely on price data.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context: 'Use this to anchor any analysis with live price...' and explicitly states it is informational, not a buy/sell signal. However, it does not explicitly mention when not to use or provide alternative tool names.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations (readOnlyHint, idempotentHint, destructiveHint) already indicate a safe, read-only operation. Description adds rich behavioral details: tracks ~80 investors, computes directional score blending ownership breadth and net activity, lists returned fields. No contradictions.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Description is concise and well-structured: purpose paragraph, usage context, clearly labeled Args/Returns/Examples sections. No unnecessary words; every sentence adds value.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given a single-ticker tool with two parameters and an existing output schema, the description fully covers purpose, data source, scoring logic, output fields, and usage examples. No gaps remain.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Input schema already provides clear descriptions for both parameters (ticker format, response_format options). Description does not add significant meaning beyond restating schema and providing usage examples. With high schema coverage, baseline of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
Clearly states 'Check which renowned value investors hold a stock and their recent activity', using a specific verb and resource. Easily distinguishes from sibling tools like analyst consensus, options signals, etc.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides explicit context with examples ('Do any famous investors own Berkshire?') and explains the tool's data source (Dataroma) and output (directional score). Lacks explicit when-not-to-use guidance, but implied by sibling coverage.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already include readOnlyHint, openWorldHint, idempotentHint, and destructiveHint. The description adds critical context: the API key requirement and the 'no signal' fallback, as well as detailing the return fields (score, confidence, data with subfields). This goes beyond the annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured with separate paragraphs for purpose, requirements, args, returns, and examples. It is not overly verbose, but the 'Args' section partly repeats the input schema. Overall, it is efficient and front-loaded with the key purpose sentence.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has an output schema (not shown), the description still provides a clear list of return fields. The single required parameter is explained, and the dependencies (API key) are noted. All necessary information for an agent to select and invoke the tool is present.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, but the description includes an 'Args' section that explains both ticker and response_format. It also provides examples (e.g., ticker='AMD') that add practical meaning beyond the schema's own descriptions, which are already present. The description compensates adequately for the coverage gap.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description begins with 'Get Wall Street buy/hold/sell consensus for a stock (Finnhub).' This clearly specifies the action (Get), the resource (consensus), and the data source (Finnhub). It is easily distinguishable from sibling tools such as equity_get_quote or equity_valuation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description states 'Requires FINNHUB_API_KEY; returns "no signal" if unset or no coverage,' providing clear conditions for use. However, it does not explicitly compare to sibling tools or give when-not-to-use advice, which would elevate the score.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, and destructiveHint. The description adds valuable behavioral context: it fans out to sources in parallel, blends signals, and reports missing sources as 'no signal' without guessing. No contradictions.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured with a clear main purpose, detailed explanation, and separate sections for args and returns. Every sentence adds value, and it's appropriately sized without being verbose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity and available schema/annotations/output schema, the description provides sufficient context: it explains the composite analysis, handling of missing data, and output formats. No gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is high (both parameters have descriptions in schema). The description adds value by providing usage examples and clarifying the ticker case-insensitivity and output format options, but it does not introduce new parameter semantics beyond what schema already provides.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool runs a full multi-source analysis of a stock and returns a scored verdict. It specifies the output range (-100 to +100), the sources used (price context, insider Form 4 activity, superinvestor holdings), and how missing data is handled. It also distinguishes itself from sibling tools.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly states 'prefer it for "should I look at X?" questions; use the individual tools when you only need one dimension.' This provides clear guidance on when to use this tool versus alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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