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PipesHub MCP Server

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pipeshub_search

Idempotent

Search indexed documents semantically to locate files by name, topic, or phrase, returning ranked records with IDs and links for retrieval.

Instructions

Vector / semantic search across the org's indexed documents.

Use this when the user wants to LOCATE a document — by name, topic, or a phrase to grep for — and to resolve it to a recordId. For open-ended questions across many documents, use pipeshub_chat instead, which does the retrieval internally and grounds the answer in citations.

Typical uses:

  • Resolve a doc name / topic into a recordId for pipeshub_get_record_content — step 1 of any full-document task (summarize, extract, review, "what does the doc say?").

  • Resolve a filename / phrase into a recordId for pipeshub_download_record.

  • Show the user a ranked list of matching files when they ask "find / search for X".

Not for structural questions — what is under this epic, which pages are in this space, what links to this ticket. Ranking by content cannot show how records relate; use pipeshub_get_record_content mode:"navigate".

A ranked sample, never a complete list. Hits are the top-scoring blocks from the best-matching records — not all blocks of any record, and not every record that matches. Never count them to answer "how many" / "all" / "every"; navigate the record group instead, which reports its real total.

The response is trimmed to one row per hit: { recordId, recordName, score, snippet, mimeType, webUrl, ... }. Highest score first; multiple hits may share the same recordId (different blocks of the same record).

When presenting results to the user, link each record using its webUrl (when present).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
appsNoSource-scoping ids — connector instance UUIDs and / or `knowledgeBase_<orgId>`. Get them from `pipeshub_sources`.
limitNoMax number of result chunks. Default 10. Use a small value (5–10) when the goal is to resolve a filename / topic into a recordId.
queryYesNatural language query. Vector search across the org's indexed records.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv2.3.3

TDQS

A4.8/5.0
Behavior5/5

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

Despite annotations providing idempotentHint and destructiveHint, the description goes much further: it discloses that results are 'a ranked sample, never a complete list', that hits are top-scoring blocks rather than all matches, and that counts should not be used for 'how many'/'all'/'every' answers. This is rich behavioral context 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.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is long but well-structured with bolded headers and bullets, and every section carries operational value. It is front-loaded with the core purpose before caveats. Some minor redundancy with the schema's query description exists, but the density is justified by the important sampling caveats.

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?

With no output schema, the description compensates by giving the response row shape, ordering, and the possibility of duplicate recordIds across hits. It also covers result presentation via webUrl and warns against misusing the ranked sample for counts. For a search tool with three parameters, this is complete.

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?

Schema coverage is 100% and each parameter already has a meaningful schema description, so the baseline is 3. The description adds extra value by recommending small limit values (5–10) for filename/topic resolution and noting that apps come from pipeshub_sources, which helps agents choose parameters more effectively.

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 opens with a specific verb and resource: 'Vector / semantic search across the org's indexed documents', and clarifies the tool's primary purpose: LOCATE a document and resolve it to a recordId. It explicitly differentiates from pipeshub_chat and pipeshub_get_record_content, so an agent can distinguish it from siblings.

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?

The description gives explicit when-to-use guidance ('Use this when the user wants to LOCATE a document') and when-not-to-use guidance ('Not for structural questions'), naming pipeshub_chat and pipeshub_get_record_content mode:navigate as alternatives. It also lists typical use cases, leaving no ambiguity about selection.

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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