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openpitch-mcp

openpitch-mcp

by Avierovich
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

get_provenance

Read-onlyIdempotent

Retrieves underlying claims and confidence factors behind a specified metric, enabling verification of data sources.

Instructions

Underlying claims + confidence factors behind a metric.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "company_id": "<company_id>",
      "metric": "<metric>"
    },
    "name": "get_provenance"
  }
}

Input Schema

NameRequiredDescriptionDefault
metricYes
company_idYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

B3/5.0
Behavior3/5

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

Annotations already indicate the tool is readOnly, idempotent, and non-destructive. The description adds that it retrieves 'claims + confidence factors', which provides context beyond annotations, but does not detail any special behaviors like data freshness, ordering, or error conditions.

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 extremely concise at 6 words, front-loading the core concept. However, it sacrifices parameter and usage details, making it perhaps too terse. It earns its place but could be expanded without losing conciseness.

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 no output schema and 0% schema coverage, the description is incomplete. It explains the purpose but fails to provide usage guidelines, parameter semantics, or any details about return structure. This leaves significant gaps for an AI agent.

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

Parameters1/5

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

Schema description coverage is 0% and the description does not explain the two parameters (metric, company_id). It implicitly references 'metric' but gives no details on allowed values, format, or relationship to other parameters. The description adds negligible value 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 tool retrieves 'underlying claims + confidence factors' behind a metric, using a specific verb (get) and resource (provenance). This distinguishes it from sibling tools like get_metric (which gets the metric value) or what_moved (which shows changes).

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, nor does it mention prerequisites or limitations. Sibling tools exist but no explicit when-to-use or when-not-to-use advice is given.

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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