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Glama

crossref

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?

Beyond the readOnly/idempotent annotations, the description discloses the embedding model (BGE-base-en), chunking (500-char overlapping windows), truncation at 200K chars with a flag, and the return structure (passages with offsets and similarity scores). 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?

The description is information-dense but not bloated; it front-loads the core action, then provides use case, workflow, and implementation details. Every sentence earns its place.

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?

For a tool without an output schema, the description adequately explains return values (top-N passages with offsets/scores), truncation behavior, and the recommended integration with ask_pipeworx_grounded. It leaves no critical gaps for a search operation.

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%, providing a baseline of 3. The description adds real-world examples for text and clarifies query as natural-language, which helps an agent understand parameter usage, but it largely reinforces schema details rather than adding entirely new semantics.

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, with specific examples (SEC 10-K, article). It distinguishes from sibling tools by emphasizing 'INSIDE' and pairing with ask_pipeworx_grounded as a complementary workflow.

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 for the prompt, and explains the benefit of saving context with offsets for verification. It also names the complementary tool ask_pipeworx_grounded, indicating a recommended workflow and alternative.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All crossref toolsInspect
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TDQS

A3.8/5.0
Disambiguation2/5

Several tools overlap heavily: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, discover_tools, and suggest_questions all serve queries, with ask_pipeworx_beta explicitly identical to ask_pipeworx. Prediction-market tools (polymarket_edges, polymarket_arbitrage, polymarket_fill_risk, bet_research, etc.) also have unclear boundaries, making tool selection tricky for an agent.

Naming Consistency4/5

Tool names are consistently lowercase snake_case with a mostly verb-first pattern (get_work, search_works, list_subscriptions, validate_claim). Minor deviations exist: some names are noun-first (entity_profile, recent_changes) and prefixes vary (get/search/list/ask/scan), but the overall convention is predictable and readable.

Tool Count2/5

34 tools is far too many for a server named 'crossref', and only three tools actually relate to Crossref. The rest form a sprawling utility belt covering data routing, memory, subscriptions, prediction markets, AI visibility, and npm scanning — a scope mismatch that makes the server feel like a kitchen sink rather than a focused offering.

Completeness2/5

The Crossref-specific surface is thin: search_works, get_work, and get_journal cover discovery and metadata but lack citation lookup, author search, and funder information. The broader Pipeworx surface is extensive but has no unifying domain, so it's impossible to consider the overall toolset complete for any coherent purpose.

Resources