secfinapi-mcp

Server Quality Checklist

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Latest release: v0.1.0
Disambiguation5/5
Each tool targets a distinct financial statement or aspect of company data (balance sheet, cash flow, income statement, metrics, company info, and company listing). There is no overlap in purpose, so an agent can easily select the appropriate tool.
Naming Consistency5/5
All tools follow a consistent verb_noun pattern with snake_case (e.g., get_balance_sheet, list_companies). No mixing of conventions or vague names.
Tool Count5/5
Six tools cover the core financial statements and metadata needed for US public company analysis. The count is well-scoped—neither too few nor excessive for the domain.
Completeness5/5
The tool set provides complete coverage of standard financial statements (balance sheet, income statement, cash flow), plus financial metrics and company metadata. There are no obvious gaps for fundamental financial analysis.
Average 3.5/5 across 6 of 6 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 2 commits in the last 12 weeks
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
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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

Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description bears full responsibility. It states the tool returns 50+ ratios but does not disclose if it is read-only, what happens on invalid ticker, whether all ratios are returned at once, or if there are rate limits. The behavioral traits are insufficiently described.
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 listing examples, which is concise and front-loaded. No extraneous words. However, it lacks structured formatting (e.g., bullet points) that could enhance readability.
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 tool's single parameter and no output schema or annotations, the description is incomplete. It does not mention date ranges, time periods, or output format (object/array). Siblings like get_balance_sheet often include date parameters, and this omission could confuse agents. More detail is needed.
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?
The input schema already describes the ticker parameter with examples (AAPL, MSFT, BRK.B), achieving 100% schema coverage. The description adds 'US public company' context but does not provide additional semantic detail beyond the schema. 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?
The description clearly states the tool provides '50+ financial ratios and metrics' for a US public company, with a list of examples (ROE, ROIC, margins, etc.). This directly differentiates it from sibling tools like get_balance_sheet or get_income_statement, which return raw financial statements.
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?
No explicit guidance on when to use this tool versus siblings. The description implies it is for ratios rather than raw data, but does not state exclusions or alternatives. This is acceptable but could be improved with explicit 'use this for ratios, use get_income_statement for revenue details'.
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?
With no annotations provided, the description carries full burden for behavioral disclosure. It mentions the data source (SEC EDGAR XBRL) and that data is 'parsed' and 'standardized', but omits critical traits such as data freshness, error handling (e.g., unfilled data for missing filings), rate limits, or typical response format. This leaves an agent uncertain about the tool's behavior.
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 sentence of 14 words that efficiently conveys the tool's purpose, source, and key content. Every word is meaningful; no fluff or redundancy. It is appropriately front-loaded and highly concise.
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?
Despite good schema coverage and conciseness, the description lacks completeness for an agent. There is no output schema, so the agent needs clues about the response structure (e.g., array of periods, detailed breakdown). The description only hints at the three main categories but not how they are organized or what fields are included. This is a notable gap for financial data retrieval.
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 description coverage is 100% as both parameters (ticker, period) have descriptions. The tool description adds no additional meaning beyond what the schema already provides. Therefore, baseline score of 3 is appropriate; the description does not need to compensate further.
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 identifies the tool as returning a standardized balance sheet for US public companies from SEC EDGAR XBRL filings, specifying the key components (assets, liabilities, equity). It distinguishes itself from sibling tools like get_cash_flow and get_income_statement through the explicit mention of balance sheet items and the standardized format.
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 for retrieving balance sheet data but does not explicitly provide guidance on when to use this tool versus alternatives (e.g., get_income_statement for income statement data). The context is implied by the tool name and description, but no 'when not to use' or alternative references are given.
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?
With no annotations provided, the description must disclose behavioral traits. It mentions the data source (SEC EDGAR XBRL) but does not discuss data freshness, error handling, rate limits, or what happens when input is invalid. This leaves significant gaps for an AI agent.
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 concise sentence (18 words), front-loaded with key information. No unnecessary words or repetition.
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?
The tool has no output schema, so the description should cover what the result contains. It mentions the three cash flow sections but does not specify the output format (e.g., data structure, units, line items). This is adequate but incomplete.
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 descriptions for both parameters (ticker and period). The description does not add additional meaning beyond what the schema already provides, 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 identifies the tool's function: providing a standardized cash flow statement for US public companies, parsed from SEC EDGAR XBRL. It explicitly mentions the three main categories (operating, investing, financing) and distinguishes itself from sibling tools like get_balance_sheet and get_income_statement.
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?
No explicit guidance is given on when to use this tool versus alternatives. The usage is implied by the tool's name and description, but there is no 'when to use' or 'when not to use' context provided.
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 the description carries the full burden of behavioral disclosure. However, it only describes the output content without mentioning any behavioral traits such as read-only nature, authentication requirements, rate limits, data freshness, or potential errors. This is a significant gap for a tool that presumably accesses external data.
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, well-structured sentence that front-loads the purpose (Company metadata for a US public company) and lists key fields efficiently. No unnecessary words or repetition.
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 low complexity (one parameter) and no output schema, the description provides a solid overview of the returned fields. However, lacking annotations, it would benefit from additional behavioral context (e.g., data source freshness, read-only hint) to be fully 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?
The input schema has 100% coverage (ticker parameter described), so the description does not need to add parameter details. The description does not elaborate on the ticker parameter beyond what the schema already states, which is acceptable given the baseline of 3.
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 retrieves company metadata for a US public company, listing specific fields (legal name, SIC/industry, exchange, fiscal year end, S&P 500 membership, link to EDGAR). It uses a specific verb (get) and resource (company info), and the listed fields distinguish it from sibling tools like financial statements.
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 for retrieving company metadata but does not provide explicit guidance on when to use this tool versus alternatives, nor does it mention any prerequisites or exclusions. Without such context, an agent might not know when this is the appropriate choice.
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 the description carries full burden. It mentions data source (SEC EDGAR XBRL) and that each period includes a filing date, but lacks details on rate limits, authentication requirements, data latency, or any limitations. For a read-only data tool, more behavioral context is needed.
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 two sentences, free of redundant information, and front-loads the core purpose. Every word 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 no output schema, the description effectively communicates the data fields included (revenue, cost of revenue, etc.) and the data source. It could be improved by mentioning return format (e.g., JSON structure) but is otherwise complete for a simple retrieval tool.
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 description coverage is 100%, with both parameters documented in the schema. The description adds context about the data content (e.g., 'standardized income statement') but does not enhance parameter understanding beyond what the schema provides (ticker and period with enum). 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?
The description clearly states the tool retrieves a standardized income statement (revenue, cost of revenue, operating income, net income, EPS) for US public companies from SEC EDGAR XBRL filings. It differentiates well from siblings like get_balance_sheet or get_cash_flow.
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 for income statement retrieval but provides no explicit guidance on when to use this tool versus alternatives (e.g., when to use quarterly vs annual). No exclusions or prerequisites are mentioned.
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 bears full burden. It discloses no behavioral traits such as read-only nature, rate limits, pagination, or data freshness. Only states basic functionality.
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, front-loaded with the core action and filter option. No extraneous words.
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?
For a simple listing tool with two optional parameters and no output schema, the description is largely complete. Could benefit from mentioning that omitting search returns all companies, but this is implied by 'list'.
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 description coverage is 100%, and the parameter descriptions in the schema are clear. The tool description merely paraphrases the search parameter without adding new meaning; the limit parameter is not mentioned at all.
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 that the tool lists or searches US public companies in SECfinAPI, with optional name/ticker filtering. Distinguishes well from sibling tools focused on financial statements.
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 clear context that the tool can list all companies or search by name/ticker. Does not explicitly mention when not to use it or alternative tools, but the differentiation from siblings is implied.
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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