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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?

Annotations declare readOnlyHint=true and destructiveHint=false, which the description reinforces by describing it as a search tool. The description adds value by noting the keyless/free tier and 12-hour cache, providing behavioral details beyond the annotations. 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?

The description is extremely concise, using a single sentence followed by a compact list of sources, modes, and key features. Every piece of information earns its place, with no redundant or irrelevant content.

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?

Given the tool's complexity (8 parameters, 2 enums, output schema exists), the description covers the high-level purpose, data sources, modes, and operational features (keyless, cache). It does not explain return values, but that is handled by the output schema. The async parameter is mentioned only in the schema, which is acceptable.

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 descriptions in the input schema (100% coverage), so the baseline is 3. The description adds high-level context about modes and sources but does not elaborate on individual parameters beyond what the schema already provides. It meets the baseline without significant enhancement.

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 it performs multi-source scientific literature search (Recherche bibliographique multi-sources) and lists specific sources (OpenAlex, Semantic Scholar, arXiv, PubMed, CrossRef) and modes (search, meta_analysis, citation_network, expert_finder). This distinctly sets it apart from siblings, most of which are business or technical tools.

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 specifies the search modes and mentions keyless/free tier with LRU cache, providing context on when to use it. However, it does not explicitly state when not to use it or compare it to alternative tools like research_paper_qa or web_search_multilang, so guidance is clear but lacks exclusions.

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

C2.8/5.0
Disambiguation2/5

Many tools have overlapping purposes, especially in competitive intelligence, ESG, and risk assessment. For example, there are multiple tools for competitor analysis (competitive_deep_dive, competitor_intel, competitor_moves, etc.) with unclear boundaries. Agents would struggle to select the correct tool without deep understanding of subtle differences.

Naming Consistency2/5

Tool names are a mix of English and French, and follow no consistent pattern. Some use snake_case (e.g., abm_architect, action_plan_esg), while others are verb-focused (e.g., content_catalog, fx_rate). The lack of a uniform naming convention makes it hard for agents to predict tool names.

Tool Count1/5

With 271 tools, the server is excessively large. Even for a broad knowledge domain, this number of tools makes discovery and selection inefficient. Typical coherent servers have 3-15 tools; this has an order of magnitude more, indicating poor scoping.

Completeness3/5

The tool set covers many domains (compliance, finance, marketing, HR, etc.), but the coverage is uneven due to redundancy. Key areas have multiple overlapping tools, while some sub-domains may still have gaps. Overall, the surface is broad but not well-curated.

Resources