PaperGraph MCP

workspace_get_paper_map

Generate an evidence-first first-load map for a stored paper, revealing theorem dependencies and candidate relationships. Use it to load a paper's theorem graph and inspect key connections.

Instructions

Return an evidence-first first-load map for one stored paper.

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": {
      "paper_id": "<paper_id>"
    },
    "name": "workspace_get_paper_map"
  }
}

Input Schema

Name	Required	Description	Default
`paper_id`	Yes
`max_candidates`	No

Schema Changelog

Changes observed during successful MCP inspections.

Changed2 schema fields changedv1.2.2Oct 17, 2026
- addedInput schema / properties / max_candidates / maximum
```
Added value: +20
```
- addedInput schema / properties / max_candidates / minimum
```
Added value: +1
```
Addedv0.10.0Sep 10, 2026

TDQS

C2.4/5.0

Behavior2/5

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

With no annotations, the description carries the full disclosure burden, yet it says nothing about side effects, permissions, cost, determinism, or what 'first-load' and 'evidence-first' mean operationally. The only hint is that results are ordered around evidence.

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?

One sentence with no wasted words, but it is short through under-specification rather than through disciplined editing; the cost is that essential meaning is missing.

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?

No output schema, no annotations, two undocumented parameters, and an undefined central concept ('map'). An agent cannot predict the shape, size, or semantics of the return value before calling.

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

Parameters2/5

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

Schema description coverage is 0%, so both parameters are undocumented. The description implies paper_id selects the stored paper but says nothing about max_candidates, its default of 5, or its cap of 20 — leaving an agent to guess what happens when it is set low.

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?

It gives a verb ('Return') and a resource ('map for one stored paper'), which is better than a tautology, but 'evidence-first first-load map' is undefined jargon that never says what the map actually contains or how it differs from the sibling workspace_get_paper.

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?

The phrase 'first-load' faintly implies this is an entry-point call, but there is no statement of when to use it versus workspace_get_paper, workspace_get_evidence, or workspace_get_result_reading_path, and no prerequisites or exclusions.

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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