discovery

How many works discuss a claim — discourse volume, with the works named

get_claim_warrant

Read-only

How many works discuss a claim — discourse volume, with the works named. For a construct pair mined from the book corpus: how many distinct works discuss the link, and which ones, with each work's stated rationale. ⚠ This is DISCOURSE, not evidence — book frequency is confounded by publication incentives, fashion and citation cascades, so it is reported as 'discussed in N works' and never as corroboration, confidence, or a score. Single-work claims are returned, labelled as such; corroboration is not a gate. No query returns corpus-level stats including a counting discrepancy we state openly.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "get_claim_warrant"
  }
}

Input Schema

Name	Required	Description
`to`	No	Target construct, e.g. employee_turnover. Use with `from`.
`from`	No	Source construct, e.g. employee_engagement. Use with `to`.
`focus`	No	Discourse volume for one focus area.
`construct`	No	Every pair the corpus links this construct to, most-discussed first.

Output Schema

Name	Required	Description	Default
`count`	No
`found`	No
`label`	No
`pairs`	No
`query`	No
`stats`	No
`caveat`	Yes
`warrant`	No
`contract`	Yes
`salience`	No
`statsNote`	No
`citationNote`	No
`focusSalience`	No
`citationCoverage`	No

Schema Changelog

Changes observed during successful MCP inspections.

First observedSep 8, 2026

TDQS

A4.3/5.0

Behavior5/5

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

The description goes well beyond the read-only and non-destructive annotations, explicitly stating that results are discourse, not evidence, that book frequency is confounded, and that results are never reported as corroboration, confidence, or score. It also discloses that single-work claims are returned and that corpus-level stats are not returned, including an openly stated counting discrepancy.

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 dense but efficient; each sentence contributes either the core function, the return content, or critical caveats. The warning is front-loaded and reinforced without becoming redundant enough to hurt usability.

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 output schema exists and annotations already mark this as read-only and non-destructive, the description sufficiently covers return semantics, limitations, and the non-evidentiary nature of the data. It could be more explicit about how the four schema parameters map to the three query modes, but the schema descriptions mostly fill that gap.

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 covers all four parameters with descriptions, so the baseline is 3. The description adds useful context about construct pairs and focus areas, but it does not substantially deepen the meaning of individual parameters beyond what the schema states.

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 counts how many distinct works discuss a claim and names them with rationale. It also draws a sharp distinction from evidence or corroboration, making its purpose unique among the sibling tools.

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 gives clear context about when the tool is appropriate: for discourse volume from the book corpus, not for evidentiary support. It warns that corroboration is not required, but it does not explicitly name alternatives or provide explicit when-not-to-use guidance.

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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