citation_graph
Citation graph via Semantic Scholar.
Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| paperId | Yes | ||
| direction | No |
tollbooth
Schema Changelog
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
- Added
- Removed
- Added
TDQS
C2.2/5.0
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It fails to state whether the tool is read-only, has rate limits, or any side effects. For a tool that likely queries an external API, this is a significant gap.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is very short (one phrase), which could be considered concise, but it lacks essential structure and detail. It does not front-load critical info or use formatting to aid readability.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the lack of output schema and simple parameter set, the description should explain what the returned citation graph contains (e.g., list of papers, metadata). It does not, making it incomplete for the agent to understand the tool's output.
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?
Schema description coverage is 0%, and the description does not explain the parameters. 'paperId' and 'direction' are not defined anywhere, leaving the agent to guess their meaning (e.g., what values for 'direction', how to obtain a paperId).
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Citation graph via Semantic Scholar' indicates the tool retrieves a citation graph for a paper, but it does not specify what type of graph (e.g., citing papers, references, or both). This vagueness makes it less clear compared to sibling tools like 'paper_details' which have more specific names.
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 guidance on when to use this tool versus alternatives like 'search_papers' or 'paper_details'. The description does not mention prerequisites or typical use cases, leaving the agent to infer usage from the name alone.
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
C2.2/5.0
Disambiguation3/5
Tools cover very diverse domains (weather, FDA, legal, crypto, etc.), so cross-domain confusion is low. However, within domains there is notable overlap: multiple food recall tools (food_recall_check, food_safety), multiple weather tools (weather_current_global, weather_forecast_grid, weather_alerts, weather_bias), and several Polymarket-related tools. This can cause agent misselection.
Naming Consistency2/5
Naming is inconsistent: some tools use verb_noun (search_arxiv, scrape, validate_agent_manifest), others use noun phrases (smart_money, space_weather, tide_data), and some are long descriptive phrases (cross_platform_arb_scan, polymarket_event_scan). No single pattern is followed, making predictions difficult.
Tool Count1/5
95 tools is excessively high for any coherent purpose. The server appears to be a random aggregation of APIs with no clear scope. Such a large catalog overwhelms agents and dilutes utility; most tools could be split into specialized servers.
Completeness2/5
Although many domains are touched, each is covered only shallowly. For example, weather lacks historical data, legal lacks case details beyond court opinions, and financial lacks stock prices. There are obvious gaps like no user authentication or data persistence. The tool set feels like a collection of endpoints rather than a cohesive service.