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Glama

Webflow

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

A4.6/5.0
Behavior5/5

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

Beyond annotations (readOnlyHint, idempotentHint, etc.), the description adds rich behavioral details: uses BGE-base-en embeddings, cosine similarity over 500-char overlapping windows, caps input at 200K chars with truncation flagging, and returns character offsets for verification. No contradiction with 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 efficient with 4-5 sentences, front-loaded with the core purpose. Every sentence adds value, though the pairing note could be slightly more concise. Overall well-structured.

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?

The description covers purpose, use case, technical details (embedding model, windowing, cap), output format (passages with offsets and scores), and pairing with a sibling tool. Despite lacking an output schema, the description provides enough context for an agent to use the tool correctly.

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 thorough descriptions for all three parameters. The tool description does not add significant new semantic meaning beyond what the schema already provides, so a baseline of 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 does semantic search inside a fetched record, provides concrete examples (SEC 10-K body, article), and distinguishes from siblings by mentioning it pairs with ask_pipeworx_grounded. It explicitly says when to use (when record is too big for prompt).

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 use: when the record is too large for the prompt. It also specifies an alternative workflow: pairing with ask_pipeworx_grounded. The description guides the agent to first fetch the text, then search within it.

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

A3.5/5.0
Disambiguation2/5

Several tools have blurry boundaries: ask_pipeworx and ask_pipeworx_beta are explicitly identical right now, polymarket_edges/polymarket_arbitrage/polymarket_edge_tracker/polymarket_fill_risk all target overlapping prediction-market signals, and ai_visibility_check vs scan_competitor_ai_presence plus entity_profile vs recent_changes vs compare_entities partially duplicate each other. The Webflow tools are distinct, but the dominant Pipeworx cluster is hard to navigate.

Naming Consistency2/5

Naming mixes multiple conventions: clean verb_noun for Webflow tools (list_sites, get_collection_item), an ask_pipeworx family, plain single verbs (remember, recall, forget), and long noun-cluster names for prediction markets (polymarket_arbitrage, polymarket_edge_tracker). There is no single predictable pattern across the set.

Tool Count1/5

36 tools is excessive for a server named 'Webflow' — only about six tools actually concern the Webflow CMS (list_sites, get_site, list_collections, list_collection_items, get_collection_item, generate_llms_txt). The remaining ~30 tools form a completely different data-research/prediction-market/memory suite, making the server wildly over-scoped and mislabeled.

Completeness2/5

For the Webflow domain, the surface is read-only: sites and collections can be listed and items fetched, but there are no create, update, delete, or publish operations, leaving obvious lifecycle gaps. The extensive non-Webflow tools do not address the stated server purpose, so the mismatch hurts completeness rather than fixing it.

Resources