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sheetsdata-mcp

by io.github.octoco-ltd

confirm_datasheet_upload

Destructive

Confirm a datasheet upload started via request_datasheet_upload. Pass the upload_token you got back from the request step. The server downloads the uploaded bytes, re-hashes to verify integrity, validates that it's a real PDF with the MPN on the first page, creates the private Document + Component records, charges the upload fee (50¢), and queues extraction.

Success response: document_id, mpn, sha256, file_size_bytes, status='pending'. Poll check_extraction_status with the MPN to wait for extraction to finish (30s-2min typically).

Failure modes:

  • 'upload_not_found' — no bytes at the upload URL yet. Retry your curl upload.

  • 'sha256_mismatch' — uploaded bytes hash differs from expected_sha256. Re-compute the hash and re-request.

  • 'invalid_pdf' — bytes aren't a parseable PDF. No charge.

  • 'mpn_not_in_pdf' — MPN (or its stem) isn't on the first page. Either you uploaded the wrong file or it's a scanned image-only PDF. No charge.

  • 'token_expired' — upload token is older than 15 minutes. Restart via request_datasheet_upload.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
upload_tokenYesOpaque token returned by request_datasheet_upload.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Added

TDQS

A4.9/5.0
Behavior5/5

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

Annotations mark this as destructive and not read-only, but the description adds specific behavioral details: it creates private Document + Component records, charges a 50¢ upload fee, and queues extraction. It also discloses token expiry (15 minutes) and which failures incur no charge, going beyond the 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?

The description is well-structured: an overview sentence, a success response block, and a failure-mode list. Every sentence provides actionable information without filler, and the bullet-style failure modes are easy to scan. It is appropriately concise for a tool with multiple side effects and error conditions.

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?

Without an output schema, the description fully specifies the success response (document_id, mpn, sha256, file_size_bytes, status) and directs to check_extraction_status for progress. It enumerates all likely failure modes with conditions and remedies, making the tool's behavior completely predictable.

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 describes upload_token as an opaque token from request_datasheet_upload (100% coverage). The description adds practical semantics: the token ties to the uploaded bytes, has a 15-minute expiry, and must be passed as-is, enriching the schema's information.

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 clearly states the tool confirms a datasheet upload initiated by request_datasheet_upload, specifying the exact verb 'Confirm' and the resource. It also distinguishes this step from the request and check_extraction_status siblings by outlining the subsequent actions (creating records, charging fee, queuing extraction).

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?

It explicitly places this tool after request_datasheet_upload and instructs to poll check_extraction_status upon success. Each failure mode includes a remediation step (e.g., retry curl upload, re-request, restart via request_datasheet_upload), providing clear next actions and alternatives.

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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TDQS

A4.8/5.0
Disambiguation5/5

Each tool has a clearly distinct purpose: search_parts finds parts, search_datasheets searches extracted content, read_datasheet reads sections, analyze_image extracts visual data, compare_parts batches comparisons, find_alternative finds substitutes, check_design_fit validates specs, prefetch/check/manage extraction status, and request/confirm handle uploads. The descriptions cross-reference each other to further eliminate ambiguity.

Naming Consistency5/5

All 12 tools follow the verb_noun snake_case pattern consistently (analyze_image, check_design_fit, compare_parts, prefetch_datasheets, request_datasheet_upload, etc.). No mixing of camelCase, verb styles, or vague generic names.

Tool Count5/5

12 tools is ideal for a datasheet-centric server, covering the full workflow from part discovery and datasheet extraction to analysis, validation, comparison, and upload. The count is neither bloated nor thin, and every tool earns its place.

Completeness5/5

The tool surface is comprehensive for the domain: part search, spec-based semantic search, detailed part data, datasheet reading (section/search), image analysis, design-fit checking, part comparison, alternative finding, extraction lifecycle management, and manual datasheet upload (request+confirm). No obvious dead ends or critical missing operations.

Resources