Typecast
nilayp-codes/typecast
Validates job-posting JSON against a strict schema; returns one fixable error line per problem.
- GitHub stars
- 1
- tools
- 2
- transport
- STDIO
- last updated
- Nov 6, 2026
Tools
2 toolsPublic tool metadata for what this MCP can expose to an agent.
get_job_posting_schemaThe JSON Schema of a job posting.
The JSON Schema of a job posting.
No parameter schema in public metadata yet.
validate_job_postingChecks a job-posting JSON against the JobPosting schema and returns one-line errors an agent can act on.
Checks a job-posting JSON against the JobPosting schema and returns one-line errors an agent can act on.
No parameter schema in public metadata yet.
Sponsors
Promote your product hereReach 125k+ visitors monthlyPromote your product hereReach 125k+ visitors monthly
Agent CaffeineSponsoredClose your lid, come back to finished work
SnugSponsoredThe clipboard manager for the AI eraPromote your product hereReach 125k+ visitors monthly
WaxSponsoredBlock anything with supervision, without erasing your iPhonePromote your product hereReach 125k+ visitors monthly
knobsSponsoredSee your site on any phone, any day, in any language
Agent CaffeineSponsoredClose your lid, come back to finished workSnugSponsoredThe clipboard manager for the AI eraPromote your product hereReach 125k+ visitors monthlyWaxSponsoredBlock anything with supervision, without erasing your iPhonePromote your product hereReach 125k+ visitors monthlyknobsSponsoredSee your site on any phone, any day, in any languageReadme
Typecast
LLM agent that turns messy job descriptions into schema-validated, typed JSON — and measures how often that fails.
Key findings (two Qwen models via OpenRouter, 30 real job descriptions):
- Schema mode makes output valid, not correct. In setup C both models were valid 98.9% of the time, at lower cost than retry, yet end-to-end accuracy was 67.1% (7B) and 84.7% (235B).
- Error-feedback retry fixed the small model, not the large one. Setup B was 98.9% valid for the 7B but 88.9% for the 235B, which failed on jd_002 in all four setup-B runs. A count-aware retry message did not change that.
- No run reached my 90% field-accuracy goal (best: 87.3%), and runs vary. The same setup moved by about 8 points end to end between runs, so a gap between two single runs is not conclusive.
60-second walkthrough:
https://github.com/user-attachments/assets/c4a2572c-88a2-49a3-8e20-176010877154
CLI and package name: soa · MCP server on PyPI: typecast-mcp
Details
- Repository
- github.com/nilayp-codes/typecast
- Categories
- Communication & Messaging, Data & Analytics
- Package
typecast-mcp- Transport
- STDIO
- Registry
- MCP registry, active
