Skip to main content
Glama

sci_literature_search

Read-only

Recherche bibliographique multi-sources sur la litterature scientifique. Sources : OpenAlex (200M+ works) · Semantic Scholar · arXiv · PubMed · CrossRef. Modes : search | meta_analysis | citation_network | expert_finder. Keyless / free tier. Cache LRU 12h.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
modeNoMode de recherche. Defaut: search
asyncNoIf true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.
queryYesKeywords, titre, auteur, DOI (ex: 10.xxxx/xxxx accepte)
domainNoDomaine scientifique. Defaut: all
date_toNoDate ISO fin (YYYY-MM-DD)
date_fromNoDate ISO debut (YYYY-MM-DD)
max_resultsNo5-50. Defaut: 20
min_citationsNoNombre minimal de citations

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
modeYes
queryYes
papersYes
statusYes
expertsNo
sourcesYes
meta_analysisNo
quality_scoreYes
citation_networkNo

TDQS

A4.2/5.0
Behavior4/5

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

The annotations already establish readOnlyHint=true and destructiveHint=false. The description adds useful behavioral context: 'Keyless / free tier' reveals authentication requirements, and 'Cache LRU 12h' discloses caching behavior. 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 two sentences with a bullet-like structure, front-loading the purpose and then listing sources and modes. Every phrase adds value (sources, modes, keyless, cache) without redundancy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description covers purpose, sources, modes, and operational constraints (keyless, cache) for a fairly complex 8-parameter tool. Since an output schema exists, return values needn't be described. It doesn't mention async behavior, but the schema documents that.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

All 8 parameters have schema descriptions (100% coverage), so the schema does the heavy lifting. The description repeats the mode list but adds no new parameter-specific information beyond the schema. Baseline 3 is appropriate.

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 defines the tool as a multi-source bibliographic search on scientific literature, listing specific sources (OpenAlex, Semantic Scholar, arXiv, PubMed, CrossRef) and modes. This distinguishes it from general web search tools like web_search_multilang and QA tools like research_paper_qa.

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?

The description mentions the main use case (scientific literature search) and lists four modes (search, meta_analysis, citation_network, expert_finder) that indicate different analytical uses. It doesn't explicitly state alternatives or exclusions, but the context is clear enough for an agent to decide when to use it.

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

Try in Browser

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

C2.4/5.0
Disambiguation1/5

Over 50 tools share the identical template 'Gapup agent-payable C-suite expertise' with similar French descriptions and reference cases, making their boundaries indistinguishable. Clusters like competitor_intel, competitive_deep_dive, competitor_moves, competitor_profiles, competitor_pricing_radar, competitor_pricing_scrape, and competitor_recommendations heavily overlap in purpose.

Naming Consistency1/5

Names are chaotic: mix of French and English, snake_case and camelCase, verb_noun, noun, and adjective forms with no uniform pattern. Examples like 'bp_narratif', 'content_enrichment', 'ai_governance_full_report_async', and 'job_result' show no coherent naming convention.

Tool Count1/5

271 tools is far beyond any reasonable MCP server scope, creating an overwhelming selection burden for agents. This count vastly exceeds the 25+ threshold for 'too many' and makes navigation impractical.

Completeness2/5

While the server covers many business domains, it lacks lifecycle operations (e.g., no update/delete tools for the deliverables it generates) and the input specifications are vague ('documented case fields' without documentation), creating functional dead ends. The sheer breadth does not compensate for these gaps.