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Glama

Wikiquote

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. First observed

TDQS

A4.9/5.0
Behavior5/5

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

Annotations already indicate safe, read-only, idempotent behavior. Description adds valuable context: truncation at 200K chars with flag, embedding model (BGE-base-en), windowing (500-char overlapping), cosine similarity, and offsets. 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.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Three sentences: first states core purpose, second provides usage context and alternatives, third gives technical details. No filler. Every sentence is essential and informative.

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?

Even without output schema, description explains return format (passages with offsets and similarity scores). Covers limitations (200K char cap, truncated flag). For a tool with moderate complexity, this is complete and sufficient.

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 adds value with natural-language query examples and clarifies the output includes offsets and similarity scores. Goes beyond the schema by explaining the embedding approach.

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?

Clearly states it performs semantic search inside a fetched record. Uses specific verb ('search within'), identifies the resource ('a fetched record'), and provides concrete examples (SEC 10-K, article). Distinguishes from sibling tools like ask_pipeworx_grounded.

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 says when to use: 'when the record is too big to cram into the prompt'. Mentions alternative sibling (ask_pipeworx_grounded) and gives a pairing strategy. Provides clear context for decision-making.

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

B3/5.0
Disambiguation2/5

The tool set mixes four Wikiquote tools with 31 unrelated Pipeworx/Polymarket tools, creating a confusing dual identity. Within the research tooling, ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded are near-synonyms, and polymarket_arbitrage, polymarket_edges, polymarket_edge_tracker, polymarket_fill_risk overlap heavily, making selection ambiguous.

Naming Consistency3/5

All names are snake_case, but patterns vary: some are verb-first (ask_pipeworx, compare_entities, resolve_entity), some noun-first (entity_profile, polymarket_arbitrage, quote_of_the_day), and several are bare verbs or nouns (search, summary, remember, quotes). Prefix groups like ask_pipeworx* and polymarket_* are consistent, but the overall convention is mixed.

Tool Count2/5

35 tools is excessive for a server named Wikiquote, as only 4 tools (quote_of_the_day, quotes, search, summary) actually serve that domain. The remaining 31 form an unrelated general research and prediction-market toolkit, making the set feel bloated and off-scope for its stated name.

Completeness1/5

As a Wikiquote server, the surface is severely incomplete: there is no random quote, page listing, author/topic browsing, or any write/update operations, and the few quotation tools are buried among unrelated functionality. The unrelated research tools may be internally rich, but they do not address the server's stated purpose.

Resources