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Glama

Lobsters

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

A4.7/5.0
Behavior5/5

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

Adds significant context beyond annotations: model details (BGE-base-en), window size (500-char overlapping), cap (200K chars with truncation flag), and offset-based verification. All consistent with idempotent and read-only hints.

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?

Front-loaded purpose, followed by usage guidance, then technical details. No wasted words; every sentence earns its place.

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, but description clearly states return type (top-N passages with offsets and similarity scores). Covers input constraints, behavior, and pairing with sibling tools. Complete for a search tool.

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%, so baseline is 3. Description enhances with examples for query, default/range for limit, and max chars for text, adding practical value.

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?

Clear verb+resource: 'semantic search INSIDE a fetched record'. Distinguishes from siblings by pairing with ask_pipeworx_grounded and contrasting with whole-document usage.

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

Usage Guidelines4/5

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 big to cram into the prompt'. Provides usage scenarios but lacks explicit when-not-to-use statements.

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

A3.6/5.0
Disambiguation2/5

Many tools have overlapping purposes, e.g., three tools for querying Pipeworx data (ask_pipeworx, ask_pipeworx_grounded, deep_research), and six tools for Polymarket prediction markets. The boundaries between them are unclear, causing potential for misselection.

Naming Consistency3/5

Tool names are all snake_case but follow inconsistent patterns: some use verb_noun (get_hottest, get_newest), some are single verbs (remember, forget), and others are noun_noun (bet_research, polymarket_arbitrage). While readable, the lack of a consistent pattern adds confusion.

Tool Count2/5

With 34 tools, the server is overloaded, especially given its name 'Lobsters' suggests a focus on that site, yet only 4 tools are Lobsters-related. The majority belong to Pipeworx, Polymarket, and generic utilities, making the scope extremely broad and unfocused.

Completeness2/5

For the implied domain of Lobsters, the tool set is incomplete (missing create/update/delete). For the broader domains (Pipeworx, Polymarket), the set is extensive but doesn't align with the server's name. The lack of a coherent domain leaves obvious gaps relative to any single purpose.

Resources