fbi-crime-mcp-server

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

Adds substantial behavior beyond the readOnly/idempotent annotations: zeros are included for every CDE category, groupings are tallied separately and their totals can differ (so one grouping cannot substitute for another), and unrecognized state/ORI values silently return zeros. That last point is a real trap the agent would otherwise fall into.

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?

Five sentences, each carrying distinct load: what is returned, the grouping-total caveat, the scope/identifier mapping, and the zero-ambiguity warning. Lead sentence establishes resource and time range before drilling into caveats; no filler.

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 an 8-parameter, multi-scope aggregation tool this covers everything an agent needs: scope requirements, date semantics, offense narrowing, and the two caveats (differing grouping totals, silent zeros) that would otherwise cause misinterpretation. An output schema exists, so return shape needs no explanation.

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?

Schema coverage is 100%, so baseline is 3; the description earns above that by noting the offense parameter narrows results, tying each scope to its one permitted identifier, and warning that bad state_abbr/ori values yield zeros rather than errors — meaning that goes beyond the schema.

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?

Opens with a specific verb+resource: arrest counts reported to FBI CDE, scoped to nation/state/agency over a monthly window, with named breakdowns (offense, sex, race, age group). The resource is clearly distinct from siblings like fbi_get_crime_estimates or fbi_get_hate_crimes, and fbi_search_agencies is named as the ORI lookup path.

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?

Gives concrete scoping rules (national omits identifiers, state needs only state_abbr, agency needs only ori) and routes the agent to fbi_search_agencies for the ORI. It does not explicitly say when to prefer this over sibling datasets such as crime estimates, but the context is unambiguous.

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?

Beyond annotations, the description discloses rich behavioral details: pagination via the 24,000-byte prefix rule, next_from behavior, oversize_notice handling, null values for current-year missing data, case-insensitivity, validation (start ≤ end), and incomplete participation caveats. This is exactly the kind of context that helps an agent use the tool correctly.

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 dense but mostly front-loaded and each sentence serves a purpose. Some redundancy exists—the offense list is repeated from the schema enum—but overall it is appropriately sized for a tool with this complexity.

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?

With an output schema already describing return structure, the description focuses on operational behavior, edge cases, and parameter dependencies. It covers everything an agent needs to call the tool correctly, including pagination, null semantics, and scope-specific requirements.

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?

Schema coverage is 100%, so the baseline is 3. The description adds cross-parameter constraints not fully captured in the schema: it ties scope to permitted identifiers, states that identifiers are case-insensitive, and enforces start no later than end. These additions meaningfully improve parameter understanding.

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 specific data returned: monthly offense rates, raw counts, population denominators, and provider coverage from FBI CDE. It doesn't explicitly name sibling tools or contrast them, but the unique resource and scope details make its purpose distinguishable.

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?

It provides conditional parameter rules (e.g., national scope omits identifiers; state requires state_abbr; agency requires ori) and explains include_comparisons. However, it never explicitly states when to choose this tool over siblings like fbi_get_arrests or fbi_get_hate_crimes, leaving that to inference.

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?

Annotations already cover readOnly/idempotent/openWorld, but the description adds a genuinely important behavioral warning: CDE returns zeros both when nothing was reported and when a state or ORI is unrecognized, so zeros are ambiguous. It also discloses that every CDE category is emitted with zeros included, which affects how results must be interpreted.

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 dense sentences, front-loaded with the resource and time window, then return contents, then scope/parameter rules and the zero caveat. No filler or restated title.

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?

With an output schema present and 100% parameter coverage, the description only needs to cover interpretation risks and scoping rules—which it does, including the zero-vs-unrecognized ambiguity that an agent could not infer from structured fields alone.

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?

Schema coverage is 100%, so baseline is 3, and the description adds the conditional applicability rule across the three scopes in one place and clarifies that bias narrows counts (including multiple-bias incidents containing the code). Most of that detail is still duplicated from the per-parameter schema text.

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?

States a specific verb+resource (hate crime counts from FBI CDE) plus scope (nation/state/agency), time granularity (monthly, Jan 1991–present), and the exact breakdowns returned. That is clearly distinguishable from the arrest, crime-estimate, LEOKA and NIBRS siblings.

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?

Gives concrete conditions for each scope mode ("national omits identifiers; state requires only state_abbr; agency requires only ori") and routes ORI lookup to the sibling fbi_search_agencies. It stops short of saying when to prefer a different FBI dataset over this one, so it is clear context without explicit exclusions.

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?

Annotations already establish read-only, idempotent, open-world behavior. The description adds meaningful data caveats: both periods return a CDE YTD totals block separately from month-specific breakdowns, raw dod/doi totals have unspecified interpretation, and classified deaths remain in the trend/region series. These are useful behavioral disclosures beyond the structured annotations.

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 dense but front-loaded with the resource scope, then period semantics, then data caveats. Every sentence contributes useful information, though the long opening sentence could be slightly more scannable.

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?

With an output schema present, the description need not explain return values. It covers the key caller-facing concerns: supported time range, period/month interaction, comparison-year behavior, and caveats about dod/doi and classified deaths. It is largely complete for a three-parameter analytical 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?

Schema coverage is 100%, so the baseline is 3. The description goes further by clarifying the relational semantics of period and month: monthly includes the same month of the comparison year, and both periods return the CDE YTD totals block. This adds value beyond the schema's per-field descriptions.

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 resource as LEOKA officer fatality data and enumerates the included dimensions (trends, weapons, activity, circumstances, demographics, regions, time breakdowns). It does not explicitly differentiate itself from sibling FBI tools such as fbi_get_arrests or fbi_get_hate_crimes, but the subject matter is specific enough to distinguish it.

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?

It explains how the period parameter works ('ytd' requests a year; 'monthly' requires month and includes comparison-year data), which gives operational guidance. However, it does not state when to choose this tool over alternative FBI datasets or any exclusions/preconditions beyond period mechanics.

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?

Beyond the read-only/idempotent/open-world annotations, it discloses the returned breakdown dimensions, zero-inclusion behavior, independent tallies per dimension, and concrete no_data failure cases. This is substantial behavioral context for an agent invoking the 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 dense but front-loaded with purpose, return shape, scope rules, and failure behavior. Every clause carries operational value for this relatively complex query tool.

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?

It covers scope selection, required offense, temporal range, returned breakdown dimensions, and expected failure behavior. With the output schema and annotations also present, an agent has enough context to invoke this tool 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?

Schema description coverage is 100%, so the schema already documents the parameters thoroughly. The description reinforces required offense, scope-specific identifier rules, and ORI lookup, but mostly repeats information available in the schema.

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 names a specific operation and resource: NIBRS incident breakdowns for one offense across national, state, or agency scope. It clearly distinguishes the tool from broad crime, arrest, hate crime, and LEOKA siblings by focusing on NIBRS incident breakdown dimensions.

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?

It gives clear usage conditions for each scope and explicitly directs agency lookup to fbi_search_agencies, plus failure conditions for no_data. It does not explicitly compare when to use this versus each sibling analysis tool, but the context is clear.

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?

The description provides significant behavioral context beyond the annotations. It details the pagination mechanism (total match count, next_offset), specific data fields returned for each agency (NIBRS status), and a precise edge case handling protocol for unrecognized CDE codes.

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 highly informative, which necessitates some density. However, it is well-structured by front-loading the core purpose and then detailing the mechanism and outcomes.

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 complex directory service with multiple inputs and paginated results, the definition is extremely complete. It covers the function, the filtering, the data fields, and the termination condition (unrecognized code), leaving minimal ambiguity for the agent.

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?

Although schema coverage is 100%, the description adds critical semantic value by detailing the filtering scope (name, county, and type) and specifying the sorting order (agency name then ORI). This helps the agent refine complex input parameters.

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 resource ('law enforcement agencies') and the primary output ('nine-character ORI codes'). It also explicitly links the result to the dependent sibling tools (fbi_get_crime_estimates, etc.), providing strong context for selection.

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?

Usage is highly guided because the description outlines its function as a necessary prerequisite for several other tools. By stating that it provides the specific 'ORIs' needed for other API calls, it defines the essential use case for the agent.

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