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Glama

Dblp

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 declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, destructiveHint=false. The description adds technical details: BGE-base-en embeddings, cosine similarity, 500-char overlapping windows, 200K char cap with truncation and flagging, and return of offsets and 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?

Single paragraph, about 4 sentences, front-loads purpose and usage, then technical details. No unnecessary words; every sentence is 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?

With no output schema, description explains return values (top-N passages, offsets, similarity scores). Also covers constraints (200K char limit, truncation) and pairing with another tool. Fully 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 description coverage is 100%. The description adds extra meaning: examples for the query parameter, clarification that text is the document, and that limit controls passage count. Baseline 3, plus 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?

The description clearly states 'Semantic search INSIDE a fetched record', specifying the verb (search within), resource (record), and the parameters. It distinguishes from sibling tools like ask_pipeworx_grounded by mentioning pairing.

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 'Use when the record is too big to cram into the prompt' and 'Pairs with ask_pipeworx_grounded', providing clear when-to-use and alternatives.

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

A3.8/5.0
Disambiguation3/5

Many tools overlap in general purpose—ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, and validate_claim all retrieve factual data—though their descriptions draw clear mode distinctions. The Polymarket family is similarly dense but each member has a distinct role. An agent must read carefully to pick the right one, but the boundaries are mostly decipherable.

Naming Consistency4/5

Tool names overwhelmingly follow snake_case verb_noun or domain_noun patterns (search_publications, resolve_entity, polymarket_edges, ask_pipeworx). Minor exceptions like the bare verbs recall and forget break the pattern slightly, and mixed prefixes (pipeworx_, ask_, search_) are still predictable.

Tool Count2/5

At 34 tools, the set is heavy, but the deeper problem is scope mismatch: the server is named Dblp yet only 3 of 34 tools (search_authors, search_publications, search_venues) relate to DBLP. The remaining 31 tools form a broad general-purpose data platform that dwarfs and obscures the apparent purpose.

Completeness2/5

For a DBLP server, the surface is minimal: only search operations exist, with no record fetch-by-id, citation metrics, or author profile detail beyond what search returns. The bulk of the toolset addresses unrelated domains, leaving the actual DBLP workflow thin and incomplete.

Resources