stockscope-mcp

Server Quality Checklist

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Latest release: v1.1.0
Disambiguation5/5
Each tool targets a distinct aspect of stock analysis: financials, filings, comparison, insider trades, historical trends, and sector peers. There is no overlap, and descriptions clarify the unique purpose of each.
Naming Consistency5/5
All tool names follow a consistent 'stock_<noun>' pattern (e.g., stock_financials, stock_filings). The naming is predictable and easy to understand.
Tool Count5/5
With 6 tools, the set covers core stock research functionalities without being too sparse or overwhelming. Each tool serves a clear purpose.
Completeness4/5
The tools cover financials, filings, insider transactions, historical trends, sector peers, and comparisons—sufficient for fundamental analysis. Minor gaps like price or news exist but don't undermine the core domain.
Average 3.4/5 across 6 of 6 tools scored.
See the Tool Scores section below for per-tool breakdowns.
This repository is archived. Archived repositories automatically receive an F maintenance tier.
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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

Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden but only states the basic function. It does not disclose behavioral traits such as result format, pagination, rate limits, or authentication requirements, which are critical for proper invocation.
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 a single sentence that gets to the point, leaving no fluff. It could be slightly more informative, but it's efficient and front-loaded.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the presence of five siblings and no output schema, the description is too sparse. It does not specify what information is returned (e.g., list of tickers, company names), how many results, or error handling, making it incomplete for reliable agent use.
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?
Schema coverage is 100% as the only parameter 'query' is described as 'Company name or ticker'. The description adds no further semantic value, so baseline score 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?
The description clearly states the verb 'Find' and the resource 'companies in the same industry sector (SIC code)', which unambiguously defines the tool's purpose and distinguishes it from sibling tools that return financials, filings, or historical data.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives. Despite having sibling tools like 'stock_compare' that might be confused with 'peers', the description offers no comparison or exclusion criteria.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden. It discloses that the tool returns financial history with key metrics, but lacks details on data frequency (annual vs. quarterly), data source, permissions, or rate limits. Adequate but not comprehensive.
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?
Single sentence is concise and front-loaded with core purpose. However, it omits important details (e.g., output format) that could be included without significant expansion. Efficient but slightly under-specified.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple tool with two parameters and no output schema, the description covers intent but fails to describe return values structure (e.g., time series format, data units). Missing output details reduces completeness, though complexity is low.
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?
Schema coverage is 100%, so description adds minimal value beyond schema. It mentions 'years' implicitly but does not elaborate on syntax or defaults. Baseline 3 is appropriate as the description does not enhance parameter understanding significantly.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states the tool retrieves multi-year financial history and trends for US public companies, listing specific data points (revenue, net income, assets, CAGR). While it distinguishes from some siblings (e.g., stock_compare), it does not explicitly differentiate from stock_financials, leaving slight ambiguity.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No explicit guidance on when to use this tool versus alternatives. No mention of prerequisites, limitations, or scenarios where another sibling tool would be more appropriate. The description implies use for trend analysis but does not provide decision criteria.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Without annotations, the description carries full burden for behavioral disclosure. It only states the action and data type, but does not explain how recent 'recent' is, whether authentication is needed, rate limits, or data completeness. For a tool with no annotations, this is insufficient.
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 a single, front-loaded sentence with no unnecessary words. It efficiently conveys the core purpose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The absence of an output schema means the description should describe return value structure or fields. It mentions 'insider transactions' but not typical fields like date, insider name, transaction type, or volume. It also lacks information on pagination or time range. For a tool with one parameter, completeness is poor.
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?
Schema coverage is 100% with a single parameter described. The description adds no extra meaning beyond the schema's parameter description, which already explains both company name and ticker examples. Baseline 3 is appropriate as the description does not harm but does not significantly enhance understanding.
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 verb 'Get', the resource 'recent insider transactions (Form 4 filings)', and the scope 'for a US public company.' It effectively distinguishes from sibling tools like stock_financials or stock_history by specifying the exact filing type.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It does not mention prerequisites, when to avoid it, or provide examples of use cases. Sibling tool names offer implicit context, but the description itself is silent on usage boundaries.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so description carries full burden. It describes a read operation (get filings) but does not disclose any potential side effects, rate limits, authentication requirements, or scope limitations (e.g., 'recent' undefined). Minimal behavioral context.
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?
Two concise sentences, no fluff. Front-loaded with the core purpose, then details about return fields. Every sentence 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?
No output schema is provided, but description adequately informs the agent about return types (filing type, date, document links). For a simple read tool with one required parameter, this is sufficiently complete.
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?
Schema coverage is 100% and the description of the 'query' parameter matches the schema ('Company name or ticker'). The description adds no additional meaning beyond the schema, so baseline 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?
Description clearly states it gets recent SEC filings, lists specific types (10-K, 10-Q, 8-K, S-1), and specifies return fields. This distinguishes it from siblings like stock_financials (financial data) or stock_history (historical prices).
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?
Description implies use for SEC filings of US public companies but provides no explicit when-to-use or when-not-to-use guidance, nor mentions alternatives. Adequate but lacks exclusions or context differentiation.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description must disclose behavior. It states it returns financial data, implying a read operation, but does not mention authentication, rate limits, data freshness, or if it's limited to specific exchanges. The lack of annotations and behavioral details makes it less transparent, but not contradictory.
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?
Single sentence listing multiple return fields without jargon. No filler or redundancy. Efficiently conveys purpose and output.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given one parameter, the description covers the input well. However, it does not specify temporal range (e.g., latest fiscal year), data format, or how to handle multiple reporting periods. The output schema is missing, but the description lists fields. For a financial data tool, more context on data scope would be helpful.
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 already describes the query parameter with examples. Description adds context: the data source (SEC EDGAR) and that it's for US public companies, which helps avoid misuse with non-US companies. This adds value beyond 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?
Clear verb 'Get financial data' specifies the action and resource 'US public company from SEC EDGAR'. Lists specific financial metrics, distinguishing it from sibling tools like stock_filings (filings) and stock_compare (comparisons).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use versus siblings like stock_filings or stock_history. No mention of prerequisites or limitations (e.g., only recent data). The description simply states what it does without usage context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description bears the full burden of behavioral disclosure. It lists the financial metrics shown, which is helpful, but does not cover error handling (e.g., invalid tickers) or data availability limitations.
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 two sentences, front-loaded with the main action. It is efficient and avoids fluff, though it could be slightly more structured (e.g., listing metrics more explicitly).
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the low complexity (2 string params) and no output schema, the description adequately conveys purpose and main output. However, it does not explain the format of the comparison (e.g., table, percentages) or any limitations, which would be helpful.
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 input schema already provides 100% coverage with descriptions of parameters 'company_a' and 'company_b' as names or tickers. The description adds value by specifying these are US public companies and hinting at the metrics shown, which goes 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?
The description clearly states the verb 'Compare' and the resource 'financial data of two US public companies side by side'. It mentions specific metrics (revenue, net income, margins, assets), distinguishing it from sibling tools like stock_financials which likely handles single companies.
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 implies usage when comparing two US companies, but lacks explicit guidance on when not to use the tool (e.g., for international companies, or when needing filings). No alternatives are named, though the sibling tools provide context.
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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