gapup-mcp

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

Name	Required	Description
`mode`	No	Mode de recherche. Defaut: search
`async`	No	If 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.
`query`	Yes	Keywords, titre, auteur, DOI (ex: 10.xxxx/xxxx accepte)
`domain`	No	Domaine scientifique. Defaut: all
`date_to`	No	Date ISO fin (YYYY-MM-DD)
`date_from`	No	Date ISO debut (YYYY-MM-DD)
`max_results`	No	5-50. Defaut: 20
`min_citations`	No	Nombre minimal de citations

Output Schema

TableJSON Schema

Name	Required	Description	Default
`mode`	Yes
`query`	Yes
`papers`	Yes
`status`	Yes
`experts`	No
`sources`	Yes
`meta_analysis`	No
`quality_score`	Yes
`citation_network`	No

TDQS

A3.8/5.0

Behavior4/5

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

Annotations already mark it as read-only and open-world. The description adds valuable behavioral context: 'keyless/free tier' and 'Cache LRU 12h', which disclose access and performance characteristics beyond the 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 extremely concise: two sentences plus a brief list of sources and modes. Every part adds value, with no unnecessary words.

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 that an output schema exists, the description need not cover return values. It adequately covers sources, modes, access (keyless/free), and caching. However, it could mention the default mode or that results are limited to 50 results (mentioned in schema). Still, it's fairly complete.

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?

Schema coverage is 100%, so baseline is 3. The description mentions modes and sources but does not add significant meaning beyond what is already in the parameter descriptions (e.g., 'query', 'domain', 'date_from') which are well-described in the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

Description clearly states it's a multi-source bibliographic search for scientific literature, listing specific sources and modes. However, it does not explicitly differentiate from related sibling 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 Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage through listed modes (search, meta_analysis, citation_network, expert_finder) and sources, but does not provide explicit guidance on when to use this tool versus alternatives, nor when not to use it.

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

C2.5/5.0

Disambiguation2/5

With 271 tools, many have overlapping purposes (e.g., multiple competitor intel tools, multiple financial modelers, multiple ESG auditors). Detailed descriptions help slightly, but the sheer volume creates confusion. Agents would struggle to select the right tool among many similar options.

Naming Consistency1/5

Tool names are wildly inconsistent: mix of English and French, snake_case and short phrases, some very generic (process, run, execute equivalents). No discernible naming convention (e.g., abm_architect vs. boundary_control vs. bp_narratif). This makes it hard to predict tool names.

Tool Count1/5

271 tools is far beyond typical well-scoped servers (3-15). This indicates an unfocused, over-bloated tool surface. Even for a general business intelligence server, this number is excessive and violates the principle of each tool earning its place.

Completeness2/5

Despite the large count, coverage feels scattered. Some domains (e.g., content, competitive intel) have many tools, while others (e.g., supply chain, HR) have gaps. The set lacks a coherent scope; it seems like a dump of many separate tool collections rather than a complete, curated surface.

Resources

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