frc-mcp

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries full weight. It discloses important behavioral aspects: the finding is a historical observation from a public dataset, not a prediction about individuals, and includes source attribution. This adds context beyond a simple 'returns summary.'

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is extremely concise, with every sentence providing unique context (the statistic, data source, caveat, attribution). It is front-loaded with the key finding and avoids fluff.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a tool with no parameters and no output schema, the description fully conveys what the summary contains, its provenance, and its limitations. It is complete for an agent to select and invoke this simple read-only tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The tool has zero parameters, so the baseline is 4. The description does not need to explain parameters, and the schema covers the (empty) parameter list.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly identifies the tool as providing a summary of the 'door effect' with specific statistics, but it lacks an explicit verb like 'returns' or 'provides.' It is unambiguous about the resource but could be mistaken for the output content itself rather than a tool description.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

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 compared to siblings like get_lender_denial_stats or get_national_fha_stats. It does not state any exclusions or alternative tool recommendations.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries full responsibility for behavioral disclosure. It thoroughly explains that the statistics are a 'historical observation computed from the public CFPB HMDA 2025 record', specifies criteria ('actions 1,2,3; loan_type 2'), adds scope limitations ('top-100 by volume'), and explicitly states it is 'not a prediction about any individual application'. This goes well beyond minimal disclosure.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is three concise sentences: the first states the core function, the second provides data source and calculation context, and the third gives attribution and a caveat. It is front-loaded and each sentence adds value with no redundant content.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description is nearly complete for a simple stats tool: it covers data source, calculation criteria, scope, and attribution. The main gap is that it does not specify what the returned statistics actually are (e.g., denial rate, counts, sample size), which would be useful since there is no output schema. Still, it is quite informative.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema covers the single 'lender' parameter completely with 'Lender name, slug, or 20-char LEI'. The description adds no new parameter-specific details, but it does reinforce the accepted input forms. Since schema coverage is 100%, the baseline of 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states it provides 'FHA denial statistics for one lender' by name, slug, or LEI. This specific verb-resource combination, plus the scope restriction to a single lender, distinguishes it from sibling tools like national or state stats.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description gives clear context by specifying it is for 'one lender' and 'top-100 by volume', implying it is not for national or state aggregates. However, it does not explicitly name alternatives or state when not to use it, so it stops short of a 5.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the full burden. It discloses the data source (CFPB HMDA 2025 record), the specific filter criteria (actions 1,2,3; loan_type 2), and importantly clarifies it is 'not a prediction about any individual application'. This is good behavioral context for a read-only statistics tool.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is two sentences, front-loaded with the core purpose in the first sentence, followed by a caveat and attribution. Every sentence adds value with no unnecessary prose.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple, parameterless statistics tool with no output schema, the description provides enough context: data source, scope, and caveat. It does not describe the exact return format, but given the tool's simplicity and the presence of sibling tools, this is not a significant gap.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The tool has zero parameters, so the schema fully documents anything relevant. The baseline for 0 params is 4, and the description adds contextual meaning about the fixed data scope, which is sufficient.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool provides 'National FHA denial statistics from the 2025 federal record', which is specific to national scope and differentiates from sibling tools like state and lender denial stats. However, it lacks an explicit verb like 'returns' or 'lists', relying on the tool name to imply the action.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description gives useful context (historical observation, not a prediction) and implies national scope, but it does not explicitly state when to use this tool over alternatives or mention any exclusions, such as 'use get_state_denial_stats for state-level data'.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden of behavioral disclosure. It discloses the data source (public CFPB HMDA 2025), the specific selection criteria (actions 1,2,3; loan_type 2), and clarifies that it is a historical observation, not a prediction. This provides substantial transparency beyond the bare tool name, though it omits details like response format or rate limits.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is three sentences long, with no redundant words. It efficiently packs the tool's purpose, data source, computation criteria, and a disclaimer into a compact, well-ordered format.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple one-parameter tool with no output schema, the description provides sufficient context: what the tool returns (state-level FHA denial statistics), how it is computed, and a caveat about its non-predictive nature. It does not describe the return value structure, but the absence of an output schema and the simplicity of the tool make this acceptable.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema only defines 'state' as a string with length constraints, but the description adds essential meaning by specifying 'US state (two-letter code)'. This clarifies the expected format and domain, compensating for the schema's lack of description and the 0% schema_description_coverage.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states 'FHA denial statistics for a US state (two-letter code)' and specifies the exact data source and calculation criteria. This unambiguously identifies the tool's function and distinguishes it from sibling tools like 'get_national_fha_stats' (national scope) and 'get_lender_denial_stats' (lender scope).

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage for state-level FHA denial statistics through its explicit 'US state' scope, but it does not explicitly contrast with alternatives or state when not to use it. The sibling tool names provide context, but the description itself lacks direct use-case guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

It discloses that data is a historical observation from the CFPB HMDA 2025 record with specific action/loan_type filters, and explicitly says it is not a prediction about individual applications. This addresses potential misinterpretation. However, it doesn't describe response structure or pagination, but the non-predictive caveat is strong.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Three sentences, each with specific content: list purpose, data source/caveat, and licensing attribution. No wasted words.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's simplicity (two optional params, no output schema), the description covers the essential facts: what it lists, sorting options, data provenance, and a critical caveat. It could be enhanced by hinting at the returned fields, but the agent has enough to invoke correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The description explains the sort parameter's semantic (denial rate or volume) which aligns with the enum values. The limit parameter is not mentioned in the description; with 0% schema coverage, the agent must infer its meaning from the name and schema constraints.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description opens with 'List covered FHA lenders sorted by denial rate or volume,' which clearly states the action (list), the resource (covered FHA lenders), and the sorting criterion. This distinguishes it from sibling tools that provide aggregate statistics rather than lender-level listings.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies a ranking use case but does not explicitly state when to choose this tool over get_lender_denial_stats or the state/national stats tools. No exclusions or alternative recommendations are provided, so guidance is only implicit.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.