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Read what was agreed

draft_scope_record
Read-only

Reads a proposal, quote, estimate, contract, or email thread and returns the terms it actually states, each traced to the words it came from. Works for whoever sent the document or whoever received it. Pass the text exactly as written — do not summarise it first. Remove names, email addresses and account numbers before pasting. Returns two lists: lines (found in the text) and not_found_in_text (things the text does not clearly say). Show both to the person and ask which lines they agree were agreed, then call check_scope_change with only those. The text is processed by an AI model to produce the lines and is deleted within 24 hours. Free, no account.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
agreement_textYesThe proposal, quote, estimate, contract, or thread, pasted as written.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
linesYes
formatYes
statusYes
partiesYes
versionYes
no_termsNo
next_stepYes
confidenceYes
lines_hashYes
not_found_in_textYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.7/5.0
Behavior5/5

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

Discloses two important behavioral facts beyond annotations: the text is processed by an AI model and is deleted within 24 hours. It also instructs the user to remove PII, clarifying data-handling expectations. This meaningfully extends the read-only perspective already marked by 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?

Every sentence earns its place: purpose, recipient orientation, exact-text handling, redaction, output shape, next step, data deletion, and account requirement. It is dense but not verbose, with no repetition of the schema and no filler.

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 single-parameter, read-only tool, the description covers all necessary operational details: input format, PII handling, output semantics, follow-up action, privacy, and cost. The output schema likely encodes structures, but the description explains the meaning and step-by-step usage, leaving no guesswork.

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?

The schema already documents the agreement_text parameter well, so the baseline is 3. The description adds useful operational guidance: pass the text exactly as written, do not summarise first, and remove names, emails, and account numbers before pasting. This goes beyond a mechanical schema-required description and makes invocation safer.

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, resource, and outcome: it reads a document (proposal, quote, contract, etc.) and returns the terms it actually states, each traced to the words it came from. It also differentiates from the sibling check_scope_change by explaining how to use that tool after.

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?

Provides a clear workflow: pass the exact text, show the returned lists to the user, ask which lines were agreed, then call check_scope_change with only those. It tells the agent when to use this tool and how to proceed, though it does not spell out explicit negative use cases.

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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