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Collective problem-solving memory for coding agents — local SQLite + sqlite-vec by default
Context8 has well-structured tool definitions with clear naming (verb_noun pattern) and comprehensive parameter schemas. All 7 tools have descriptions (194 - 280 chars, within baseline 34 - 392 range) and typed input schemas. However, output schemas are not documented, LLMs cannot predict response structure for downstream chaining. Error handling lacks recovery guidance. Some parameters (e.g., limit, confidence) have defaults but no min/max constraints. The tools follow composition patterns well (search → log → rate feedback loop), but missing output documentation and error classification prevent higher scores.
Browse Context8 records by metadata without a search query. Use to discover what problems exist for a language, framework, or tag. Returns a list of records matching the filters.
Get all known problems for a tech stack / ecosystem. Use this BEFORE starting a new project to learn what problems other agents have hit with this combination. The output is formatted so you can use it to write a CLAUDE.md section or a skill file with known pitfalls.
Log a resolved coding problem and its solution to Context8. Call this after you successfully fix a non-trivial error. This builds the collective memory so future agents can find this solution. Only log genuinely useful solutions — not trivial typos or simple syntax errors.
Report whether a previously-retrieved Context8 solution actually worked. Call after you applied a solution returned by context8_search and observed whether it fixed the problem. This feeds the worked-ratio re-ranker so future agents see the most reliable fixes first.
Search Context8 for past solutions to coding problems. Returns semantically similar problems that were previously solved by coding agents, with their solutions, code diffs, confidence scores, and per-strategy attribution. Use when you encounter an error that might have been solved before, especially after your first fix attempt fails.
Output schemas not documented. LLMs cannot predict response structure (fields, types, pagination) for downstream tool chaining or data extraction. E.g., context8_search returns 'SearchResult' objects but the schema is not visible in tool definitions.
Numeric parameters lack min/max constraints. 'limit' defaults to 5 (max 20 for search, 10 for ecosystem) but no explicit bounds in schema. 'confidence' defaults to 0.8 with no range validation. LLMs may pass invalid values.
Error handling lacks recovery guidance. No documented error responses, categorization (retryable vs fatal), or actionable next steps. E.g., if context8_search finds no results, should the LLM retry with different query, or call context8_browse instead?
Inferred effective spec: <=2025-11-25.
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-23 | D | 59 | <=2025-11-25 | v2 |
Find past Context8 records whose *solution approach* matches a description. Use this when you have a fix in mind ('add null guard', 'use exponential backoff') and want to see how other agents have applied that approach in the past.
Get Context8 knowledge base statistics — record count, health status, vector spaces.
context8_stats has empty input schema (no properties). Description is generic ('Get Context8 knowledge base statistics'). LLM cannot determine what data it returns or when to call it vs context8_browse.
No pagination guidance in tool descriptions. context8_browse and context8_ecosystem return lists but descriptions do not mention limit behavior, whether results are sorted, or how to fetch next page. Agents may assume all results fit in one call.