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Get a check's result

get_scan_result
Read-onlyIdempotent

Reads a check started with check_plagiarism or detect_ai. Use it after starting a check, and again until status is completed. Status is pending or processing while the check runs (usually 30 to 90 seconds), completed when results are ready, or failed if the check could not run, in which case the words are refunded. A completed plagiarism check returns a similarity score and up to 15 matching sources with their URLs and matched text. A completed AI check returns an overall score and up to 25 sentences with the highest likelihood. Every result includes a report_url for the full report. Only checks on the signed-in user's account can be read; an unknown scan_id returns an error. Free to call and changes nothing.

Input Schema

NameRequiredDescriptionDefault
scan_idYesThe scan_id returned by check_plagiarism or detect_ai.
wait_secondsNoOptional. Up to 20 seconds to wait for a pending check to finish before replying, so one call can return the result. Defaults to 0, which replies at once with the current status.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.6/5.0
Behavior5/5

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

Goes well beyond the annotations: it discloses the status lifecycle, expected latency (30-90s), refund behavior on failure, the ownership restriction, and that an unknown scan_id errors. It also confirms the call is free and side-effect free, reinforcing rather than contradicting readOnlyHint/idempotentHint.

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?

Front-loaded with purpose and usage, then status semantics, then return shapes. It is dense but nearly every sentence carries operational value; the return-shape detail is slightly verbose given the length of the paragraph.

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?

No output schema exists, and the description compensates by describing exactly what a completed result contains (scores, up to 15 sources with URLs, up to 25 sentences, report_url). Combined with the status and error semantics, an agent has everything needed to call and interpret it.

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 coverage is 100%, so both scan_id and wait_seconds are already documented in the schema, including the 20-second cap and default. The description adds no extra syntax or format detail for either parameter, so the baseline 3 applies.

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?

Opens with a specific verb+resource ('Reads a check') and immediately names the two sibling tools that produce the scan_id, so the agent can place it in the workflow without opening a schema. It also distinguishes the two result shapes (plagiarism vs AI) it can return.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly states when to call it ('after starting a check, and again until status is completed') and names the alternatives that start checks. The polling loop and the terminal states are spelled out, leaving nothing to inference.

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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