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Quotable

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

A4.9/5.0
Behavior5/5

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, destructiveHint. Description adds significant behavioral details beyond annotations: embedding model (BGE-base-en), similarity metric (cosine), chunking strategy (500-char overlapping windows), character limit (200K chars with truncation flag). No contradictions.

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?

Single paragraph is well-structured: purpose first, then usage guidance, then technical details. Every sentence adds value without redundancy. No wasted words.

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 lacking an output schema, the description explains return values (top-N passages with character offsets and similarity scores). It also provides technical details (embedding, chunking, limit) that are essential for correct usage. The description is complete for a search-within-text 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 description coverage is 100% (all three parameters have descriptions). The description adds helpful examples for the query parameter and clarifies the default for limit (5). It also reiterates the max character limit for text. This adds some value beyond the schema, justifying a 4 above the baseline of 3.

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 performs semantic search inside a fetched record, using specific verbs ('search inside') and resources ('fetched record'). It distinguishes from siblings by contrasting with ask_pipeworx_grounded and implying it is not for fetching data but for searching within already-fetched text.

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 big to cram into the prompt'. It also explains the benefits (saves context, returns only relevant passages) and mentions a complementary tool (ask_pipeworx_grounded) for grounding over passages.

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

A3.7/5.0
Disambiguation2/5

The ask_pipeworx family (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded) is highly overlapping—two of them are explicitly identical right now—and the six polymarket_* tools plus bet_research create unclear boundaries between prediction-market analysis tools. Company-focused tools (entity_profile, compare_entities, recent_changes, resolve_entity) also partially overlap in what they fetch, making tool selection error-prone.

Naming Consistency4/5

Most tool names follow a clear snake_case pattern with descriptive verbs (search_quotes, resolve_entity, validate_claim, subscribe, unsubscribe). There is good use of family prefixes like polymarket_* and pipeworx_*, though the Pipeworx family mixes prefix and suffix placement (ask_pipeworx vs. pipeworx_feedback), which is a minor inconsistency.

Tool Count2/5

35 tools is far too many for a server named 'Quotable', especially since only 4 tools (get_authors, list_tags, random_quote, search_quotes) relate to quotes. The rest span data lookup, prediction markets, memory, subscriptions, AI visibility, and dependency scanning—an extremely broad, unfocused scope that overwhelms the apparent purpose.

Completeness3/5

The quote-related surface is minimal but functional (random, search, authors, tags), though missing obvious operations like get_quote_by_id. The data-lookup and prediction-market domains are thoroughly covered with grounding, research, arbitrage, and fill-risk tools, so the broader set is complete—but it does not serve the server's stated identity as a quotes provider.

Resources