# specir-mcp

> specir-mcp — jietianliang-specir-mcp. Use this tool when you need to extract and query structured information from technical documents, solving problems of data retrieval and organization. It takes in technical documents and outputs a standardized intermediate representation, enabling efficient querying and retrieval of specific sections, entities, and provenance. Ideal for use cases involving document analysis, information extraction, and knowledge retrieval, where a standardized and queryable representation of technical documents is required.

Canonical page: https://skillsregistry.net/skills/jietianliang-specir-mcp  
JSON: https://api.skillsregistry.net/v1/skills/jietianliang-specir-mcp

## Description

Enables turning technical documents into a structured intermediate representation (SpecIR) and querying it via five MCP tools: specir_resolve, specir_fetch, specir_explain, specir_search, and specir_status. It provides a standardized way to extract, store, and retrieve document sections, tables, figures, entities, and provenance.

## Trust

- **Trust score (0–1):** 0.66
- **Verification tier:** scanned
- **Last scanned:** 2026-08-30

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** mcp-remote
- **Runtime environment:** api
- **Category:** media
- **Updated:** 2026-08-30

## Source

- **Source listing:** [Glama](https://glama.ai/mcp/servers/lk692tkei3)
- **Repository:** <https://github.com/jietianliang/specir-mcp>

## Use it

Resolve this record through the SkillsRegistry MCP server (no auth, read-only):

```
claude mcp add --transport http --scope user skillsregistry https://api.skillsregistry.net/mcp
```

```json
{
  "jsonrpc": "2.0",
  "id": 1,
  "method": "tools/call",
  "params": {
    "name": "get_skill",
    "arguments": {
      "slug": "jietianliang-specir-mcp"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/jietianliang-specir-mcp` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/jietianliang-specir-mcp/pull`

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SkillsRegistry indexes agent skills from public registries and GitHub. Skills we have analysed are scanned with Circle-IR and scored on six dimensions; each listing states its scan coverage. More: https://skillsregistry.net/llms.txt