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PaperGraph MCP

PaperGraph MCP

by lotchuazzz-crypto
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

workspace_add_reading_note

Add a note or question to a PaperGraph reading session, optionally attaching it to a theorem, proof, span, or other target.

Instructions

Add a note or question to a reading session.

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": {
      "session_id": "<session_id>",
      "text": "<text>"
    },
    "name": "workspace_add_reading_note"
  }
}

Input Schema

NameRequiredDescriptionDefault
textYes
note_typeNonote
target_idNo
session_idYes
target_kindNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changedv1.2.2
    • changedInput schema / properties / target_kind / anyOf
      Previous value: -[
-  {
-    "type": "string"
-  },
-  {
-    "type": "null"
-  }
-]New value: +[
+  {
+    "enum": [
+      "result_id",
+      "proof_id",
+      "span_id",
+      "external_stop",
+      "unresolved_stop",
+      "session"
+    ],
+    "type": "string"
+  },
+  {
+    "type": "null"
+  }
+]
  2. Addedv0.9.3

TDQS

C2.7/5.0
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 behavioral burden. It does not state that this is a mutating write, whether notes are editable/deletable, what a 'question' implies versus a 'note', or any auth requirements. Minimal disclosure for a mutation tool.

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?

A single efficient sentence with no waste and the core action front-loaded. Its brevity, however, edges into under-specification rather than true conciseness.

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?

A 5-parameter mutation tool with zero annotation coverage, no output schema, and no parameter documentation cannot be adequately described in one sentence. An agent lacks the information to call it correctly with target_id/target_kind.

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% across 5 parameters. The description loosely implies session_id (reading session) and note_type (note vs question), but target_id and target_kind — including the enum of target kinds — are entirely undocumented in both the description and the schema.

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?

States a specific verb (add) and resource (note or question) scoped to a reading session, so an agent knows the action. However, it makes no attempt to distinguish itself from near-siblings like workspace_record_reading_checkpoint or workspace_create_reading_session.

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?

No guidance on when to use this versus the many other reading-session tools, no prerequisites, no exclusions. The agent must infer context entirely from the name.

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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