paper_details
Paper metadata via Semantic Scholar.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| paperId | Yes |
Paper metadata via Semantic Scholar.
| Name | Required | Description | Default |
|---|---|---|---|
| paperId | Yes |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations present, the description carries the full burden of behavioral disclosure. It only states 'metadata' without specifying what fields are returned, whether the operation is read-only, any rate limits, or error handling. The description is insufficient for an agent to understand the tool's behavior beyond the basic intent.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely short (one phrase), but it is underspecified rather than concise. It lacks crucial context and fails to earn its place because it does not convey enough information to guide agent behavior. A good concise description would pack more meaning into fewer words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given that the tool has one required parameter, no annotations, and no output schema, the description should provide enough context about input and output. It does not describe what 'metadata' includes, return format, or error cases. It is incomplete for an agent to use correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
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 'paperId' parameter at all. It does not specify the format (e.g., Semantic Scholar ID, URL, DOI) or any constraints. The agent is left with no additional meaning beyond the raw schema, which is just a string type.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Paper metadata via Semantic Scholar.' clearly indicates the tool retrieves metadata for a specific paper using Semantic Scholar as the source. The verb is implied (get/retrieve), and the resource is a paper. While it does not explicitly distinguish from siblings like search_papers, the purpose is clear enough for an agent to understand it provides detailed metadata for a given paper ID.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No usage guidelines are provided. The description does not mention when to use this tool over alternatives such as search_papers or citation_graph. An agent would have to infer from the tool name and sibling context that it is for fetching details of a known paper, but there is no explicit guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
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 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.
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.
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.