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by io.github.tendercrestai
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Shred an RFP

shred_rfp
Read-only

Shred an RFP or solicitation into a numbered requirement list with a gap preview. Paste text or upload a PDF or DOCX. AI extraction can miss requirements and every requirement needs human review. This starts an analysis job and returns a result token; call get_shred_result with the token to read the requirements. Free, no sign-in. Limited to 3 shreds per day, shared with the web shredder.

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": {
      "processingConsent": "<processingConsent>"
    },
    "name": "shred_rfp"
  }
}

Input Schema

NameRequiredDescriptionDefault
fileNo
textNoSolicitation text, 40 to 100000 characters. Provide text or file.
titleNoShort title for the solicitation, up to 200 characters.
sourceNoWhere the solicitation came from, up to 500 characters.
processingConsentYesMust be true: confirms the source may be processed to extract requirements.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.5/5.0
Behavior5/5

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

Beyond the readOnlyHint annotation, it discloses the key behavioral traits an agent needs: the tool is asynchronous (starts a job, returns a token), extraction is unreliable ("AI extraction can miss requirements and every requirement needs human review"), and there is a shared quota of three per day. The quota is the practically important constraint that no annotation or schema field exposes.

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?

Five short sentences, front-loaded with what the tool produces, followed by input modes, the reliability caveat, the async mechanics, and the quota. No filler and nothing repeated from the title.

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?

For a nested-schema tool with no output schema, the definition covers the return value (a result token), the required follow-up call, input modalities, and the accuracy caveat. Nothing needed to invoke it correctly or interpret its response is missing.

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 80%, so the schema already documents data size limits, text length bounds, and the consent flag. The description adds only the high-level text-or-file choice and file formats, which the schema largely states as well, so it does not add substantial meaning beyond the structured fields.

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 names a specific verb and resource ("Shred an RFP or solicitation") and states the concrete output shape: "a numbered requirement list with a gap preview." It also distinguishes this tool from the sibling it depends on by explaining that it only starts the job and that get_shred_result reads the requirements.

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 gives clear operating context — accept text or a PDF/DOCX, no sign-in, three shreds per day shared with the web shredder — and explicitly routes the agent to get_shred_result with the returned token. What is missing is an explicit exclusion or alternative for cases like re-reading an existing result, which the agent must infer from the sibling name alone.

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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