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Glama

Vimeo

by io.github.pipeworx-io

Search Within a Source

search_within
Read-onlyIdempotent

Semantic search INSIDE a fetched record. Pass the text you already pulled (e.g. a SEC 10-K body, an article, a long tool result) plus a natural-language query; get back the top-N passages with character offsets and similarity scores. Use when the record is too big to cram into the prompt — search_within saves context, returns only the passages that matter, and every passage carries an offset so the agent can verify a verbatim quote. Pairs with ask_pipeworx_grounded: fetch with the gateway, ground over the relevant passages instead of the whole document. BGE-base-en embeddings + cosine over 500-char overlapping windows; cap is 200K chars (longer inputs are truncated and flagged).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
textYesThe document text to search inside (max ~200K chars).
limitNoMax passages to return (1-20, default 5).
queryYesNatural-language query — what passages do you want? E.g. "supply-chain risk", "fiscal year 2024 revenue", "drug interactions with warfarin".

Schema Changelog

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

  1. Added

TDQS

A4.5/5.0
Behavior4/5

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

Annotations already declare read-only, idempotent, non-destructive. Description adds algorithmic details (BGE-base-en, cosine, 500-char windows), a 200K char cap with truncation flag, and guarantees that offsets enable verification. This exceeds annotation info but does not fully describe return format (e.g., structure of passages).

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?

Single well-organized paragraph: purpose, mechanics, use case, sibling relationship, technical details. Every sentence adds value with no redundancy. Very concise for the amount of information conveyed.

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?

Covers all essential aspects: what, how, when, limitations (cap, truncation), verification (offsets), and relationship to sibling tool. Despite missing output schema, the description adequately describes return format. No gaps given the tool's complexity.

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% with good descriptions. The description provides minor extra context (e.g., examples for query, text as 'already pulled') but does not significantly enhance parameter understanding beyond the schema. Baseline 3 is appropriate.

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 performs semantic search inside a fetched record, specifying inputs (text + query) and outputs (passages with offsets and scores). It distinguishes from siblings like ask_pipeworx_grounded by positioning itself as a tool for searching within an already-fetched document.

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 tells when to use: 'Use when the record is too big to cram into the prompt.' Also provides an alternative workflow: pairing with ask_pipeworx_grounded for grounding. This helps the agent decide between sibling tools.

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

B3.1/5.0
Disambiguation2/5

Several tools have overlapping purposes: ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded are near-duplicates, and the polymarket_* family has six tools with blurred boundaries. The Vimeo tools are distinct, but the massive unrelated Pipeworx set creates ambiguity about which tool is appropriate for a given task.

Naming Consistency2/5

Tool names mix conventions: Vimeo tools use bare nouns (video, channel, user), while Pipeworx tools use verb_noun (resolve_entity, validate_claim) or noun_verb (ai_visibility_check). Some names like ask_pipeworx and pipeworx_feedback do not follow a consistent verb-first pattern.

Tool Count2/5

40 tools is far too many for a Vimeo server; only 9 tools are Vimeo-related, and the remaining 31 are an unrelated Pipeworx data toolkit. This inflates the surface area and makes the set unwieldy.

Completeness2/5

The Vimeo surface covers read operations (search, get, list) but lacks any write operations like upload, update, or delete videos. It also misses common Vimeo features like comments, likes, or portfolio management, so common tasks would hit dead ends.

Resources