Skip to main content
Glama
scitex-ai

crossref-local

by scitex-ai

cache_plot_scatter

Generate a year-vs-citations scatter plot from a local CrossRef cache, save it as an image file, and return a list of top-cited papers.

Instructions

Generate year vs citations scatter plot.

Saves plot to file and returns top cited papers.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYesCache name
top_nNoNumber of top papers to label on plot
outputYesOutput file path (png/pdf/svg)

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

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

With no annotations, the description carries the transparency burden. It discloses two key behaviors: 'Saves plot to file' and 'returns top cited papers.' This reveals side effects (file output) and return behavior, which is more transparent than many tool descriptions. However, it doesn't mention whether it overwrites files or requires an existing cache, but for a plotting tool this is adequate.

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?

Two short sentences, front-loaded with the primary purpose, and no filler. Every sentence adds value: the first states the plot type, the second describes the output and return behavior. This is highly concise and well-structured.

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 the tool is simple with 3 parameters and an existing output schema, the description covers the essential aspects: what it does, what it saves, and what it returns. It doesn't explain the output format or error cases, but the output schema fills that gap. The description is sufficient for an agent to select and invoke this tool correctly.

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 all parameters already have descriptions. The description adds minimal new parameter-level meaning beyond confirming the 'top_n' relates to top cited papers and output file path is for saving the plot. It does not further clarify formats or constraints beyond the schema, so the baseline of 3 is appropriate.

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 clearly states the tool creates a 'year vs citations scatter plot' and explicitly mentions the side effect of saving to file and returning top cited papers. This is specific, and it distinguishes from sibling 'cache_plot_network' which would be a different plot type. The verb 'Generate' and resource 'scatter plot' make the function unambiguous.

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 for creating a scatter plot of citations over time, but it doesn't provide explicit guidance on when to use this tool versus alternatives like cache_plot_network. No when/when-not conditions or alternative recommendations are mentioned, so it earns a 3 for implied usage.

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

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/scitex-ai/crossref-local'

If you have feedback or need assistance with the MCP directory API, please join our Discord server