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Glama

Lichess

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?

Annotations (readOnlyHint, idempotentHint) are consistent; the description adds crucial behavioral details: embedding model (BGE-base-en), window size (500-char overlapping), character limit (200K chars with truncation flag), and output format (passages with offsets and similarity scores).

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 concise with 6 sentences, each adding distinct value: purpose, mechanics, usage guidance, sibling pairing, and technical details. No fluff; front-loaded with core function.

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?

Despite no output schema, the description fully covers expected output (passages with offsets and scores), technical constraints (model, window, cap, truncation), and pairing with a sibling tool. All necessary context for an agent to use correctly is present.

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 adds minimal parameter-specific info beyond the schema—'max ~200K chars' for text is already in schema, and examples for query are given but not novel. No extra guidance on 'limit'.

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 'Semantic search INSIDE a fetched record' with specific verb (search) and resource (text). It distinguishes from siblings like ask_pipeworx by positioning itself as a tool for searching inside a fetched record, and it explicitly pairs with ask_pipeworx_grounded for grounding.

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 explicitly states when to use: 'Use when the record is too big to cram into the prompt'. It also explains the benefit (saves context, returns only relevant passages) and provides an example alternative workflow with ask_pipeworx_grounded.

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

B3.3/5.0
Disambiguation2/5

The server mixes chess tools with numerous data query tools from Pipeworx, causing significant overlap. Multiple ask_pipeworx variants (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded) and deep_research have similar purposes, making it difficult for an agent to choose correctly. Chess tools are distinct but compete with many unrelated tools.

Naming Consistency2/5

Tool names follow no consistent pattern: chess tools use mostly underscores (top_players, opening_explorer), Pipeworx tools use mixed styles (ask_pipeworx, deep_research, entity_profile), and memory/subscription tools use simple verbs (remember, subscribe). The naming is inconsistent across the set.

Tool Count2/5

With 41 tools, the count is high and unfocused. A chess server would typically have 10-15 tools; the remaining 31 tools from Pipeworx are unrelated and overwhelm the set. The server tries to cover too many domains, making it bloated for its primary purpose.

Completeness2/5

The chess-specific tools (10) cover basic queries but lack deeper chess analysis (e.g., puzzles, board evaluation). The extensive Pipeworx tools are out of scope for a Lichess server, resulting in an incomplete surface for the expected domain and an excessive surface for unrelated data lookups.

Resources