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Glama

Paperswithcode

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 declare readOnlyHint, openWorldHint, idempotentHint, destructiveHint. Description adds 'BGE-base-en embeddings + cosine over 500-char overlapping windows; cap is 200K chars (truncated and flagged)', providing concrete technical behavior beyond 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: purpose, use case, technical details. Every sentence adds value. No fluff.

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?

No output schema, but description explains results include character offsets and similarity scores. Also covers limitations and pairing with sibling tool. Complete for a search 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 coverage is 100%. Description adds examples for query ('supply-chain risk', 'fiscal year 2024 revenue') and clarifies that text is the document. Adds meaning beyond schema.

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, resource, and method. It distinguishes from sibling tools by mentioning pairing with ask_pipeworx_grounded and contrasting with simple fetch/scrape.

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?

Explicitly says 'Use when the record is too big to cram into the prompt' and provides workflow with sibling tool. Does not explicitly state when not to use, but context is clear.

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
Disambiguation2/5

Several tools form overlapping families: ask_pipeworx and ask_pipeworx_beta are currently identical, ask_pipeworx_grounded and deep_research cover much of the same router territory, and five Polymarket tools overlap heavily on edge/arbitrage analysis. The descriptions are detailed, but an agent would regularly need to compare multiple near-equivalent candidates before choosing one.

Naming Consistency3/5

Names are consistently snake_case and readable, but the conventions vary widely: verb_noun (search_papers, resolve_entity), noun phrases (entity_profile, polymarket_arbitrage, recent_changes), bare verbs (remember, subscribe, forget), and suffixed variants (ask_pipeworx_beta, ask_pipeworx_grounded). It is not chaotic, but there is no single predictable pattern.

Tool Count2/5

Thirty-five tools is far too many for a server named Paperswithcode, especially since only four tools actually relate to papers while the rest cover data routing, prediction markets, memory, subscriptions, npm auditing, llms.txt generation, and AI visibility. The count feels bloated and the scope unfocused relative to the server's apparent identity.

Completeness3/5

The paper-discovery subdomain is reasonably covered with search, trending, detail, and implementation lookup, and the broader set includes discovery, memory, subscription lifecycle, and feedback tools. However, the overall surface is a patchwork of unrelated domains with no well-defined boundary, and paper datasets/models can only be counted rather than directly listed. Agents can work around most gaps, but the coverage is uneven.

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