TeamMemory
PYPI · TEAM_MEMORY
Team experience database - let AI accumulate, retrieve and apply team knowledge across chats.
Available components
Last scanned (1 hour ago)
Scores higher than 84% of PyPI servers
Category Memory and Knowledge
How this component scores in each security and reliability category. Every signal is checked automatically from public evidence about the published package, including repeated runs of it in an isolated sandbox, and we only credit what we can confirm. How we score → Why this is hard to score →
Supply Chain Security100
- No malware found by supply-chain analysis.Pass
- No known CVEs affecting this package version or its production dependencies.Pass
- Runs hatchling.build at install time, a recognised build step with no custom scripting around it. View diagnostics → Pass
- 1 of 37 dependencies flagged as unhealthy. View diagnostics → Partial
Provenance & Transparency45
- Source repository is publicly reachable at the declared URL. View diagnostics → Pass
- Provenance check failed: no build-provenance attestation is published. See how to fix → View diagnostics → Fail
- Clear OSI-approved license (MIT).Pass
- Actively maintained (last published 205 days ago).Pass
- Disclosure check failed: no security disclosure policy was found in the source repository. See how to fix → Fail
Schema Quality & AI Usability81
- 100% of prompts and resources have a non-trivial description (not blank, and not just the item's name).Pass
- AI-judged instruction clarity (good).Pass
- Context-footprint check failed: tool/resource definitions use about 1823 tokens (~113/item across 16 items; 13 tools + 3 resources), over budget; trim descriptions and params. See how to fix → Fail
- Usage-examples check failed: none of the tools include examples. See how to fix → Fail
Stability & Change Management80
- Stability observed for 24 of 30 days with no destabilising changes; credit accrues until the full window elapses.Partial
Tool Coverage95
- 100% of tools have a non-trivial description (not blank, and not just the tool's name).Pass
- 84% of tool parameters carry a description.Partial
- Structured output schemas are declared (100% of tools); any adoption earns full credit.Pass
Tool Safety100
- No prompt-injection markers were found in the server instructions, tool names or descriptions we captured.Pass
- We read all 13 captured tool definition(s), and no name or description among them implies an irreversible operation.Pass
- An AI judge read all 15 captured unit(s) of tool text and found none that tries to manipulate the model reading it.Pass
Capabilities100
- Implements a current MCP spec version (2026-07-28).Pass
Every change we have recorded for this component, newest first. Security-relevant changes are always shown. ▲ marks a change for the better, ▼ a change for the worse; unmarked changes are neutral.
- 5 Nov 26 −3
- Stability: pass → 0.80 functional
- 30 Oct 26 +15
- Malware scan: unverified → pass ▲ security
- Stability: 0.97 → pass security
- 29 Oct 26 0
- Stability: pass → 0.97 functional
- 26 Oct 26 −15
- Malware scan: pass → unverified ▼ security
- 23 Oct 26 0
- Stability: 0.97 → pass security
- 22 Oct 26 0
- Stability: pass → 0.97 functional
- 19 Oct 26 0
- Security disclosure: unverified → fail ▼ functional
- 18 Oct 26 0
- Security disclosure: fail → unverified ▼ functional
Diagnostic detail from the automated scan of this channel: what the scanner observed at each step, so you can see exactly where a check passed or failed. It is informational only and never changes the trust score.
Captured 5 Nov 2026 · Analysed pypi/team_memory@0.1.2
Provenance No attestation
The registry publishes no build provenance for this version, so there is nothing to verify.
| Result | No attestation |
|---|---|
| Ecosystem | pypi |
| Reason | No attestation published |
Background: How many MCP packages publish verified provenance →
Install scripts 1 script
| Hook | Tier | Command |
|---|---|---|
| build_backend | allowlisted | hatchling.build |
Background: Why install scripts are a supply-chain risk →
Dependencies 37 packages
| Packages resolved | 37 |
|---|---|
| Stale | 1 |
| Tree resolution | Complete |
Background: SBOMs and build attestations, explained →
The tools this component advertises to a client, with an estimated token cost for each. Expand a tool to see its parameters and schema. The per-tool counts are indicative and are not scored directly; the schema's total context footprint is one signal in Schema Quality & AI Usability. A tool's description is untrusted text the model reads on every call, which is what makes this list a security surface and not just an inventory: how tool poisoning works →
tm_claim Tm Claim ~56
Claim an experience/problem so other agents know you're working on it. Claims auto-expire after 30 minutes of inactivity.
| Name | Type | Req | Description |
|---|---|---|---|
| experience_id | string | yes | The experience ID to claim. |
| message | – | – | Optional message describing what you're doing. |
| Name | Type | Req | Description |
|---|---|---|---|
| result | string | yes | – |
No examples provided.
tm_config Tm Config ~22
Read runtime retrieval configuration snapshot (retrieval/search/cache/pageindex-lite).
Input schema present but exposes no named parameters.
| Name | Type | Req | Description |
|---|---|---|---|
| result | string | yes | – |
No examples provided.
tm_feedback Tm Feedback ~78
Provide feedback on a searched experience — rate 1-5 (5=best). This improves future search results. Returns ~50 tokens.
| Name | Type | Req | Description |
|---|---|---|---|
| comment | – | – | Optional feedback comment. |
| experience_id | string | yes | The ID of the experience to rate. |
| rating | integer | yes | Rating from 1 to 5 (5 = most helpful). |
| Name | Type | Req | Description |
|---|---|---|---|
| result | string | yes | – |
No examples provided.
tm_learn Tm Learn ~134
Learn from a conversation or document: extract structured experience using LLM and auto-save to the knowledge base. Call this after solving a problem to capture the knowledge. By default saves as draft (requires review before publishing). Returns ~200-500 tokens.
| Name | Type | Req | Description |
|---|---|---|---|
| as_group | boolean | – | If True, extract as parent + children experience group. |
| conversation | string | yes | The conversation or document text to learn from (required). |
| project | – | – | – |
| save_as_draft | boolean | – | If True (default), save as draft requiring review. |
| tags | – | – | Additional tags to merge with LLM-extracted tags. |
| Name | Type | Req | Description |
|---|---|---|---|
| result | string | yes | – |
No examples provided.
tm_notify Tm Notify ~57
Notify the team that a new experience has been saved. Use after tm_save or tm_learn to signal other agents.
| Name | Type | Req | Description |
|---|---|---|---|
| experience_id | string | yes | The experience ID to notify about. |
| message | string | – | Human-readable notification message. |
| Name | Type | Req | Description |
|---|---|---|---|
| result | string | yes | – |
No examples provided.
tm_save Tm Save ~172
Quick-save a simple experience (title + problem required, solution optional). Use this for fast knowledge capture — solution can be added later. For typed experiences with full fields, use tm_save_typed instead. Returns ~100-200 tokens.
| Name | Type | Req | Description |
|---|---|---|---|
| code_snippets | – | – | Key code examples. |
| framework | – | – | Framework. |
| language | – | – | Programming language. |
| problem | string | yes | Problem description (required). |
| project | – | – | – |
| publish_status | string | – | "published" (default) or "draft". |
| root_cause | – | – | Root cause analysis. |
| skip_dedup | boolean | – | If True, skip duplicate detection check. |
| solution | – | – | Solution description (optional — allows incomplete experiences). |
| tags | – | – | Tags for the experience. |
| title | string | yes | Experience title (required). |
| Name | Type | Req | Description |
|---|---|---|---|
| result | string | yes | – |
No examples provided.
tm_save_group Tm Save Group ~200
Save a group of related experiences (parent + children). Use this when a solution involves multiple steps or stages. The parent describes the overall problem/solution, children describe individual steps. Returns ~200-500 tokens.
| Name | Type | Req | Description |
|---|---|---|---|
| category | – | – | Category classification. |
| children | array | yes | List of dicts, each with keys: title, problem, solution, and optionally: tags, code_snippets, root_cause. |
| experience_type | string | – | Type for the group (default "general"). |
| framework | – | – | Framework. |
| language | – | – | Programming language. |
| parent_problem | string | yes | Problem description for the parent. |
| parent_root_cause | – | – | Root cause for the parent. |
| parent_solution | – | – | Overall solution summary (optional). |
| parent_tags | – | – | Tags for the parent. |
| parent_title | string | yes | Title for the parent experience. |
| project | – | – | – |
| severity | – | – | Severity for bugfix/incident groups. |
| Name | Type | Req | Description |
|---|---|---|---|
| result | string | yes | – |
No examples provided.
tm_save_typed Tm Save Typed ~322
Save a typed experience with full fields (experience_type, severity, category, structured_data, git_refs, related_links, progress_status). Types: general, feature, bugfix, tech_design, incident, best_practice, learning. Returns ~200-400 tokens.
| Name | Type | Req | Description |
|---|---|---|---|
| category | – | – | Category (frontend/backend/database/infra/performance/security/other). |
| code_snippets | – | – | Key code examples. |
| experience_type | string | – | Type — general/feature/bugfix/tech_design/incident/best_practice/learning. |
| framework | – | – | Framework. |
| git_refs | – | – | List of git references [{type, url, hash, description}]. |
| language | – | – | Programming language. |
| problem | string | yes | Problem description (required). |
| progress_status | – | – | Progress status (type-specific, e.g., open/investigating/fixed/verified). |
| project | – | – | – |
| publish_status | string | – | "published" (default) or "draft". |
| related_links | – | – | List of related links [{type, url, title}]. |
| root_cause | – | – | Root cause analysis. |
| severity | – | – | Severity level (P0-P4, for bugfix/incident). |
| skip_dedup | boolean | – | If True, skip duplicate detection check. |
| solution | – | – | Solution (optional — allows incomplete experiences). |
| structured_data | – | – | Type-specific data dict (e.g., reproduction_steps, environment for bugfix). |
| tags | – | – | Tags for the experience. |
| title | string | yes | Experience title (required). |
| Name | Type | Req | Description |
|---|---|---|---|
| result | string | yes | – |
No examples provided.
tm_search Tm Search ~145
Search the team experience database for relevant solutions. Call this BEFORE starting to solve a technical problem to check if the team already has a solution. Returns ~1000-4000 tokens depending on result count.
| Name | Type | Req | Description |
|---|---|---|---|
| grouped | boolean | – | Return results grouped by parent-child. Default True. |
| max_results | integer | – | Maximum number of results (or groups when grouped=True). |
| min_similarity | number | – | Minimum similarity threshold. |
| project | – | – | – |
| query | string | yes | The search query. |
| tags | – | – | Optional tags to filter by. |
| top_k_children | integer | – | Max children per group. Default 3. |
| use_pageindex_lite | – | – | – |
| Name | Type | Req | Description |
|---|---|---|---|
| result | string | yes | – |
No examples provided.
tm_solve Tm Solve ~139
Smart problem solving: search the team experience database, auto-format the best solution, and mark it as used. Call this FIRST when encountering a technical problem. Returns ~500-2000 tokens (focused on top matches).
| Name | Type | Req | Description |
|---|---|---|---|
| file_path | – | – | Current file path for context enrichment. |
| framework | – | – | Framework for filtering. |
| language | – | – | Programming language for filtering. |
| max_results | integer | – | Max solutions to return (default 3, focused). |
| problem | string | yes | Description of the problem to solve (required). |
| project | – | – | – |
| tags | – | – | Optional tags to filter by. |
| use_pageindex_lite | – | – | – |
| Name | Type | Req | Description |
|---|---|---|---|
| result | string | yes | – |
No examples provided.
tm_status Tm Status ~24
Read runtime status summary for diagnostics (service/search-pipeline/cache/pageindex-lite).
Input schema present but exposes no named parameters.
| Name | Type | Req | Description |
|---|---|---|---|
| result | string | yes | – |
No examples provided.
tm_suggest Tm Suggest ~128
Get experience recommendations based on current work context. Unlike tm_search which needs an explicit query, tm_suggest builds a query from file path, language, framework, or error message. Returns ~500-2000 tokens (lightweight format).
| Name | Type | Req | Description |
|---|---|---|---|
| error_message | – | – | Error message encountered (if any). |
| file_path | – | – | Current file path (extracts directory/filename hints). |
| framework | – | – | Framework being used. |
| language | – | – | Programming language being used. |
| max_results | integer | – | Maximum suggestions to return. |
| project | – | – | – |
| use_pageindex_lite | – | – | – |
| Name | Type | Req | Description |
|---|---|---|---|
| result | string | yes | – |
No examples provided.
tm_update Tm Update ~47
Update an existing experience with additional solution details or new tags. Returns ~100-300 tokens.
| Name | Type | Req | Description |
|---|---|---|---|
| experience_id | string | yes | – |
| solution_addendum | – | – | – |
| tags | – | – | – |
| Name | Type | Req | Description |
|---|---|---|---|
| result | string | yes | – |
No examples provided.
How do I install the TeamMemory MCP server?
TeamMemory runs locally as a PyPI package, launched with uvx team_memory. Ready-made configuration for Claude, Cursor, VS Code, Codex and 5 more is on this page, copied from each client's own documentation.
pypi · team_memory
claude mcp add ysydhc-team-memory -- uvx team_memory
What is the TeamMemory MCP server?
TeamMemory is an MCP server listed in the public MCP registry as io.github.ysydhc/team-memory. Team experience database - let AI accumulate, retrieve and apply team knowledge across chats. This page covers its PyPI package (team_memory).
Is the TeamMemory MCP server safe to use?
TeamMemory scores 82 out of 100 on VerifyMCP. We found no known CVEs affecting it as of 5 November 2026. That is a record of what we were able to check automatically, not an endorsement. The category breakdown on this page shows every signal behind the number, including the ones we could not confirm.
What tools does the TeamMemory MCP server expose?
TeamMemory exposes 13 tools: tm_solve, tm_learn, tm_suggest, tm_search, tm_save, and 8 more. Their descriptions and schemas cost roughly 1,524 tokens of context every time the server is loaded.
Is the TeamMemory MCP server still maintained?
TeamMemory is still listed as active in the MCP registry. We last reached this channel on 5 November 2026. Those dates come from our own scans of the registry and the channel itself, not from anything the publisher announced.
What licence is the TeamMemory MCP server under?
TeamMemory declares the MIT licence, which is OSI-approved. That covers the source only, and says nothing about the cost of any service it calls.