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Stipple — Document Verification & Extraction

List tender data sources

tender_sources
Read-only

Every source we search, what it is allowed to do, and what the last run returned. FREE.

USE THIS WHEN someone asks where the data comes from, whether a particular portal is
covered, or why a search came back empty. It is the honesty surface: it names sources
behind login walls, sources whose robots.txt refuses us, and sources that returned
nothing on the last run and why.

Returns `{sources[], coverage}` — per source: id, tag, name, URL, refresh mode,
jurisdiction, tier, how it is accessed, what its robots.txt says, how many tenders we
hold from it, and its status on the most recent run. Snapshot sources include their
observed date and are not presented as nightly feeds.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A4.6/5.0
Behavior5/5

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

The description goes well beyond the readOnlyHint annotation, disclosing that the tool names sources behind login walls, sources blocked by robots.txt, and sources that returned empty on the last run with reasons. It also clarifies that snapshot sources are not presented as nightly feeds, adding important caveats about data freshness.

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 front-loaded with the core purpose, followed by usage, then the return shape. It is longer than minimalist but every section (purpose, triggers, output detail) carries valuable information. A 5 would require tightening the output enumeration, but it is well-organized.

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?

The tool has no parameters, a readOnly annotation, and an output schema. The description fully explains the return payload (`sources[]` and `coverage`) and the per-source fields, so an agent knows exactly what to expect. No critical usage or behavioral context is missing.

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

Parameters4/5

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

There are zero parameters, so the schema is empty and schema description coverage is vacuously 100%. Per the rubric, this earns the baseline 4; there is nothing for the description to add about parameters.

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?

States a specific verb ('List') and resource ('tender data sources'), then expands with exactly what is returned: each source, its permissions, and the last run's results. This clearly distinguishes it from the sibling tools find_tenders and match_tenders, which operate on tenders rather than source metadata.

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 explicitly says 'USE THIS WHEN someone asks where the data comes from, whether a particular portal is covered, or why a search came back empty.' This is clear trigger-based guidance. It doesn't explicitly name alternatives or when-not-to-use, but the sibling names make the exclusion obvious.

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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