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Fbi Crime

by io.github.pipeworx-io
OverviewDiscussionsAdmin

National Estimate

national_estimate
Read-onlyIdempotent

National summarized counts + rates for an offense across a date range. Useful for "how has X crime trended nationally".

Example tool call

This call uses example arguments supplied in the tool’s input schema.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "offense": "violent-crime"
    },
    "name": "national_estimate"
  }
}

Input Schema

NameRequiredDescriptionDefault
toNoEnd month: MM-YYYY, YYYY-MM, or YYYY (defaults to current month)
fromNoStart month: MM-YYYY, YYYY-MM, or YYYY (defaults to Jan, 5 years ago)
offenseYesOffense slug (e.g., "violent-crime", "murder", "burglary")

Output Schema

NameRequiredDescriptionDefault
toYesEnd year
dataYesRaw FBI CDE national estimate data
fromYesStart year
offenseYesOffense slug queried

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed4 schema fields changed
    • changedInput schema / properties / from / description
      Previous value: -"Start year (default 2020)"New value: +"Start month: MM-YYYY, YYYY-MM, or YYYY (defaults to Jan, 5 years ago)"
    • changedInput schema / properties / from / type
      Previous value: -"number"New value: +"string"
    • changedInput schema / properties / to / description
      Previous value: -"End year (default current)"New value: +"End month: MM-YYYY, YYYY-MM, or YYYY (defaults to current month)"
    • changedInput schema / properties / to / type
      Previous value: -"number"New value: +"string"
  2. Changed2 schema fields changed
    • addedInput schema / examples
      Added value: +[
+  {
+    "offense": "violent-crime"
+  },
+  {
+    "from": 2015,
+    "offense": "murder",
+    "to": 2023
+  }
+]
    • changedOutput schema / (root)
      Previous value: -nullNew value: +{
+  "properties": {
+    "data": {
+      "description": "Raw FBI CDE national estimate data",
+      "type": "object"
+    },
+    "from": {
+      "description": "Start year",
+      "type": "number"
+    },
+    "offense": {
+      "description": "Offense slug queried",
+      "type": "string"
+    },
+    "to": {
+      "description": "End year",
+      "type": "number"
+    }
+  },
+  "required": [
+    "offense",
+    "from",
+    "to",
+    "data"
+  ],
+  "type": "object"
+}
  3. First observed

TDQS

A3.9/5.0
Behavior3/5

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

Annotations fully cover safety (readOnlyHint, idempotentHint) so the description need not repeat them. It adds little new behavioral detail aside from summarizing the output (counts + rates). The description could mention rate units or timezone, but is acceptable given 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 a single sentence with a clear purpose and usage hint, no extraneous text. It is efficient and front-loaded.

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 an output schema (implied), so return format need not be described. However, details like default date ranges, rate calculation basis, or handling of unknown offenses are missing. Still, for a simple national query, it is minimally 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 with descriptions for all parameters. The description does not add extra meaning beyond the schema, hence a 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 tool provides 'national summarized counts + rates for an offense across a date range' and gives an example query ('how has X crime trended nationally'). This distinctively separates it from sibling tools like 'state_summary' or 'agency_summary'.

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 includes a usage hint ('Useful for...') that indicates appropriate context. However, it does not explicitly state when not to use the tool or mention alternatives, though the sibling list provides implicit differentiation.

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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