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Contrie

by com.contrie
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Verify extracted data against its source page

contrie_verify
Read-only

Check data you already have against the page it claims to come from, field by field. Takes data from any source plus a url (or raw html/text) and reports which values actually appear in the source and which do not, with a supporting excerpt for each. This check charges no credits. Requires authentication: sign in with your Contrie account (OAuth), or send an API key in the Authorization header. It answers "is this value present in this page", NOT "does the page say this about that": a value that is correct for a different field still reads as grounded.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlNoThe page the data claims to come from; omit if passing html or text
dataYesThe extracted data to check, as a JSON object or array
htmlNoRaw HTML of the source, max 5,000,000 UTF-8 bytes
textNoPlain text of the source, max 5,000,000 UTF-8 bytes
baseUrlNoWith html only: the page it came from, to resolve relative links; never fetched

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • addedInput schema / properties / data / anyOf
      Added value: +[
+  {
+    "additionalProperties": {},
+    "propertyNames": {
+      "type": "string"
+    },
+    "type": "object"
+  },
+  {
+    "items": {},
+    "type": "array"
+  }
+]
  2. Changed1 schema field changed
    • addedInput schema / properties / baseUrl
      Added value: +{
+  "description": "With html only: the page it came from, to resolve relative links; never fetched",
+  "format": "uri",
+  "type": "string"
+}
  3. First observed

TDQS

A4.5/5.0
Behavior5/5

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

Annotations only provide readOnlyHint and openWorldHint, so the description carries the burden of behavioral context. It adds substantial detail: the check charges no credits, requires OAuth or an API key, accepts URL/HTML/text, reports per-field results with excerpts, and applies literal-match semantics. No contradiction with 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?

Four sentences, front-loaded with the core purpose, then input modes, cost/auth, and the key semantic caveat. Every sentence adds operational value with no filler or repetition.

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

Completeness5/5

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

With no output schema, the description still tells the agent what the tool returns: which values appear in the source, which do not, and a supporting excerpt for each. Combined with the fully detailed input schema, an agent has enough to invoke this tool correctly.

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?

Schema description coverage is 100% and each parameter already has a meaningful description, including max lengths and baseUrl's 'never fetched' behavior. The description adds general context like 'url (or raw html/text)' and field-by-field checking, but not significant new parameter-level meaning beyond the schema, so baseline 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 states a specific verb ('Check') and resource ('data you already have' against 'the page it claims to come from'), with a precise method ('field by field'). This clearly distinguishes it from sibling extraction/read tools by focusing on verification rather than retrieval or extraction.

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?

It implies when to use the tool ('data you already have') and gives a clear scope limitation: it answers 'is this value present in this page', NOT 'does the page say this about that'. It does not explicitly name alternative sibling tools, but the boundary around intended use is clear.

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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