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Glama

Leetcode

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
Behavior5/5

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

The description discloses internal mechanics beyond annotations: 'BGE-base-en embeddings + cosine over 500-char overlapping windows; cap is 200K chars (longer inputs are truncated and flagged).' It also explains output structure (passages with offsets and similarity scores), adding value beyond the readOnlyHint, openWorldHint, and idempotentHint 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 a single, well-structured paragraph of five sentences. It front-loads the core purpose, then adds usage guidance and technical details. Every sentence adds value, and there is no wasted text.

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?

For a tool with three parameters and no output schema, the description covers input (text, query, limit), output (passages with offsets and scores), technical details (embeddings, windowing, cap), and context (pairing with sibling tool). It is fully self-contained and enables correct use.

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%, so baseline is 3. The description provides some extra context (e.g., truncation flag for text parameter), but mostly reiterates schema information. The examples in the schema for 'query' are already detailed.

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's purpose: 'Semantic search INSIDE a fetched record.' It specifies the verb 'search' and resource 'inside a fetched record', and distinguishes itself from siblings by pairing with 'ask_pipeworx_grounded' and noting when to use it instead of cramming text into the prompt.

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?

The description explicitly says when to use the tool: 'Use when the record is too big to cram into the prompt.' It also mentions a complementary sibling tool ('ask_pipeworx_grounded'), providing clear context. However, it does not explicitly state when not to use 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

B3.1/5.0
Disambiguation2/5

Multiple tools have overlapping roles: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research all serve the same lookup purpose, and there are six different Polymarket tools with similar names and functions. The inclusion of a large unrelated data platform alongside a few LeetCode tools makes selection additionally confusing.

Naming Consistency4/5

Almost all tools follow a consistent snake_case verb_noun pattern (ask_pipeworx, compare_entities, list_subscriptions, etc.). Minor exceptions like 'problem' and 'daily_question' are still readable and don't break the overall predictability.

Tool Count2/5

37 tools is far too many for a server named 'Leetcode' — the vast majority are unrelated Pipeworx data, prediction-market, and memory tools. Even as a general data server the count is heavy, and for the apparent LeetCode purpose it is severely over-scoped.

Completeness2/5

The LeetCode-specific tools cover basic user stats and problem details but lack problem listing/search, submissions, or any interaction beyond read-only queries. The Pipeworx side is extensive but irrelevant to the server's stated purpose, so the core domain has significant gaps.

Resources