graph-polymarket-mcp

query_subgraph

Execute custom GraphQL queries against Polymarket subgraphs to retrieve prediction market data, including market stats, trader P&L, positions, and orderbook activity.

Instructions

Execute a custom GraphQL query against a Polymarket subgraph

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": {
      "subgraph": "<subgraph>",
      "query": "<query>"
    },
    "name": "query_subgraph"
  }
}

Input Schema

Name	Required	Description
`query`	Yes	GraphQL query string
`subgraph`	Yes	Subgraph identifier: main, beefy_pnl, slimmed_pnl, activity, or orderbook
`variables`	No	Optional GraphQL variables as key-value pairs

Schema Changelog

Changes observed during successful MCP inspections.

Changed1 schema field changedv2.1.2Aug 10, 2026

changedInput schema / properties / subgraph / enum

Previous value: -[
-  "main",
-  "beefy_pnl",
-  "slimmed_pnl",
-  "activity",
-  "orderbook",
-  "open_interest",
-  "resolution",
-  "traders"
-]New value: +[
+  "main",
+  "beefy_pnl",
+  "slimmed_pnl",
+  "activity",
+  "orderbook",
+  "open_interest",
+  "resolution",
+  "traders",
+  "v2_orderbook",
+  "v2_pnl",
+  "v2_main"
+]

Addedv2.0.0May 2, 2026

TDQS

B3.3/5.0

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 disclosing behavior. It only states what the tool does ('Execute a custom GraphQL query'), but does not mention whether queries are read-only, what happens on malformed queries, rate limits, or the shape of the response. This is comparable to the 'Update shared drive settings' example, which lacked permission and side-effect context and scored 2.

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?

The description is a single, front-loaded sentence with no wasted words. It is appropriately concise for a tool whose name and schema already convey much of the structure. However, it is slightly under-specified for the behavioral side, which prevents a perfect score.

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?

While the schema covers parameters, the description does not explain the intended use case relative to siblings, the nature of a 'subgraph' query, or the response format (especially since no output schema exists). For a generic tool with many specialized alternatives, this is insufficient context for an agent to select and invoke it correctly without additional inference.

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 description coverage is 100% for all three parameters, so the schema fully documents parameter meanings. The description adds no additional parameter semantics beyond the schema. Per the rubric, a baseline of 3 is appropriate when the schema does the heavy lifting.

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 action ('Execute a custom GraphQL query') and the target resource ('Polymarket subgraph'), which is specific and distinguishes it from the list of specialized sibling tools that return fixed data types. The word 'custom' signals that this is the generic, low-level option rather than a pre-built endpoint.

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?

No explicit guidance is given about when to use this tool versus the many specialized sibling tools (e.g., get_live_orderbook, get_user_positions). The word 'custom' implies it is for queries not covered by dedicated endpoints, but this is only implied, not stated. There is no exclusion or alternative mention.

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