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research-mcp

research-mcp

by 404Simon
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

venue_proceedings

Retrieve papers published in a venue's proceedings via DBLP, filterable by year or topic, to survey conference publications and get titles, authors, and DOIs.

Instructions

List papers published in a venue's proceedings (via DBLP) — for surveying what a conference like ACM e-Energy actually accepts. Returns title, year, authors, DOI.

venue: a venue name (e.g. "e-Energy") or a DBLP stream key (e.g. "conf/eenergy"). year: optional single year to filter proceedings (e.g. 2024). query: optional keyword to only return papers matching a topic (e.g. "carbon"). Results are cached, so re-surveys are instant.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "venue": "<venue>"
    },
    "name": "venue_proceedings"
  }
}

Input Schema

NameRequiredDescriptionDefault
yearNo
limitNo
queryNo
venueYes

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.1/5.0
Behavior4/5

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

With no annotations available, the description carries the full transparency burden, and it does well: it discloses the data source (DBLP), the exact return fields (title, year, authors, DOI), and the caching behavior ('Results are cached'). It does not cover failure modes, rate limits, or data freshness, but the core behavioral profile is clearly communicated.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured and front-loaded: the first sentence states the core purpose, followed by compact parameter explanations and a useful caching note. It is slightly longer than strictly necessary due to the inline examples, but every element earns its place and none is redundant.

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 and there are no annotations, the description covers the essential call context: purpose, source, return fields, parameter semantics, and caching. The main gap is the undocumented 'limit' parameter, which may affect result completeness for large proceedings. Overall it is sufficiently complete for an agent to select and invoke the tool effectively.

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 description coverage is 0%, so the description must compensate. It does for most parameters: venue is explained with both a display name and a DBLP stream key, year is explained as an optional filter, and query is explained as a keyword filter with examples. However, the 'limit' parameter is absent from the description entirely, so one of the four parameters remains semantically underspecified.

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 opens with a specific verb and resource: 'List papers published in a venue's proceedings (via DBLP)'. This clearly differentiates the tool from siblings like search_papers, paper_details, or get_bibtex by bounding it to a venue-scoped survey operation, and it even gives a concrete usage scenario ('ACM e-Energy').

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 provides clear context by explicitly framing the tool for surveying what a conference 'actually accepts', which tells an agent when this tool is useful. However, it does not explicitly mention alternative tools or state when not to use this one, so it stops short of full when/when-not guidance.

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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