Skip to main content
Glama
RobackaB

Novelty Research MCP

by RobackaB

publication_evidence_to_session

Retrieves publication evidence for a research query and saves compact results to a SQLite session, enabling source-grounded prior-art and novelty analysis in a structured workflow.

Instructions

Získa publikačné dôkazy a uloží kompaktný výsledok do SQLite session.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryNo
run_idNo
attempt_noNo
session_idNo
max_fetchesNo
max_resultsNo
source_typeNopublication
english_queryNo
fetch_timeout_sNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.9.5

TDQS

C2.7/5.0
Behavior3/5

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

The description discloses a key side effect: it saves data to a SQLite session, which implies modifying session state. However, it does not mention whether it fetches external data, whether it overwrites existing session data, or any other potential side effects (e.g., network calls, rate limits). With no annotations, the description partially carries the transparency burden but falls short of full disclosure.

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 a single concise sentence with no wasted words. It is well-structured and directly states the action and target. It could be slightly more informative, but it is appropriately brief and does not ramble.

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

Completeness1/5

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

Given the tool's complexity (9 parameters, similar sibling tools, an output schema), the description is extremely sparse. It does not explain the purpose of each parameter, how this tool relates to sibling tools, what constitutes a 'compact result', or when to use it. The description is far from complete for an agent to select and invoke it correctly without additional context.

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

Parameters1/5

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

The input schema includes 9 parameters (query, run_id, attempt_no, session_id, max_fetches, max_results, source_type, english_query, fetch_timeout_s) but provides no descriptions for any of them, and the tool description does not explain their meaning or usage. The description coverage is 0%, leaving the agent with no semantic guidance from the description.

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?

The description clearly states the tool's action: getting publication evidence and saving a compact result to a SQLite session. The verb 'gets' and 'saves' are specific, and the resource ('publication evidence') distinguishes it from sibling tools (patent, web). However, 'compact result' is not defined, so it's not perfectly precise.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

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

There is no explicit guidance on when to use this tool versus its siblings (patent_evidence_to_session, web_evidence_to_session). The word 'publication' implies it is for publication sources, but this is only implicit and not clearly contrasted with alternatives. No context or decision criteria are provided.

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