roampal-core
Roampal Core is an MCP server that gives AI coding assistants (Claude Code, OpenCode) persistent, outcome-based memory across sessions — automatically injecting relevant context and learning from exchange outcomes. All data is stored locally and privately.
MCP Tools:
search_memory: Query across all memory collections (working, history, patterns, memory_bank, books) for relevant context.add_to_memory_bank: Permanently store user identity, preferences, and goals.update_memory: Correct or update an existing memory entry by ID.delete_memory: Remove outdated or incorrect memories by ID.score_memories: Score exchange outcomes so good advice is promoted and bad advice demoted (Claude Code uses the main LLM; OpenCode uses an independent sidecar model).record_response: Store key takeaways from significant exchanges to build historical and pattern memory over time.
Key Features:
Automatic context injection before the AI sees user messages — no manual calls needed.
Five memory collections with varying lifespans: working (24h), history (30d), patterns (persistent), memory_bank (permanent), books (permanent).
Document ingestion for permanent reference storage in the books collection.
Auto-detection of platform (Claude Code hooks / OpenCode plugin), self-healing server restarts, and CLI tools for configuration, diagnostics, and statistics.
Integrates with local models via Ollama to perform sidecar scoring of AI exchanges, allowing for private and independent evaluation of memory performance.
Benchmarks
85.8% on the corrected LoCoMo benchmark (non-adversarial, end-to-end answer accuracy) — validated on 1,986 questions across 10 conversations with dual grading. All figures in this section are sourced from the paper and roampal-labs (see citations at the bottom of this section).
Result | Score |
Conversational learning vs raw ingestion | +23 points (76.6% vs 53.0%, p<0.0001) |
Architecture vs model effect | Architecture ~10x larger contributor |
Poison resilience (1,135 adversarial memories) | -2.6 to -4.2 points only |
TagCascade retrieval (tags-first + CE rerank) | +1.9 Hit@1 vs pure CE (p<0.0001) |
Benchmark pipeline runs on a single GPU with no cloud dependencies. Roampal itself runs on CPU — no GPU required. Full methodology, data, and evaluation scripts: roampal-labs
Paper: "Beyond Ingestion: What Conversational Memory Learning Reveals on a Corrected LoCoMo Benchmark" (Logan Teague, April 2026)
Related MCP server: total-recall
Quick Start
pip install roampal
roampal initAuto-detects installed tools. Restart your editor and start chatting.
Target a specific tool:
roampal init --claude-codeorroampal init --opencode
The core loop is identical — both platforms inject context, capture exchanges, and score outcomes. The delivery mechanism differs:
Claude Code | OpenCode | |
Context injection | Hooks (stdout) | Plugin (system prompt) |
Exchange capture | Stop hook | Plugin |
Scoring | Main LLM via | Independent sidecar (your chosen model, disabled by default until configured) |
Self-healing | Hooks auto-restart server on failure | Plugin auto-restarts server on failure |
Claude Code prompts the main LLM to score each exchange via the score_memories tool. OpenCode never self-scores — an independent sidecar (a separate API call) reviews each exchange as a third party, removing self-assessment bias. The score_memories tool is not registered on OpenCode. Scoring is disabled by default until you explicitly configure it via roampal sidecar setup. During setup, Roampal detects local models (Ollama, LM Studio, etc.) and lets you choose a scoring model. Zen free models are available as an explicit opt-in choice for users without a local model or API key — they route through OpenCode's proxy which may log data. A cheap or local model works great — scoring doesn't need a powerful model.
v0.5.9: Memory footprint fix + embedder/reranker upgrade + crash observability — triggered by a
MemoryErrorcrash traced to ONNX Runtime's CPU memory arena never releasing per-shape scratch buffers, compounded by FP16 model files up-converting to FP32 at load. Disables the arena and mem-pattern cache on both models, switches the embedder to its own measured INT8 export (same mpnet model — the planned e5-base upgrade was held back to v0.6.0 after an accuracy gate caught a near-duplicate-guard regression) and the cross-encoder to its own INT8 export, and shares one cross-encoder session across all profiles instead of one per profile. Measured process footprint drops from ~2,355MB to ~484MB in isolation (~5x), and search gets faster on both the embed and rerank paths rather than trading memory for latency. A background, per-collection re-embed migrates existing vectors to the new embedder automatically on first start — never blocking the MCP client, never mixing model families within a collection. Also adds file-based logging, MemoryError/ExceptionGroup handling, an RSS heartbeat, and a/api/statusendpoint so the next incident like this one leaves a trace, and degraded states (embedder down, reranker down, migration in progress) are surfaced to the user instead of silently returning an empty result.v0.5.8: Crash-resilience release — enables SQLite WAL + FULL durability on the ChromaDB catalog so hard terminations (Windows port conflicts, external process kills, power loss) no longer corrupt or empty the database. Rewrites
SessionManager.mark_scored()to perform an atomic temp-file replace, guaranteeing the JSONL transcript survives a crash mid-write. Also ships 16 new automated tests covering both fixes, fixes pre-existing test debt that left the full suite red on Windows, and adds dev tooling (pytest-timeout,pytest-forked,build,twine). No data migration required; WAL is applied on the next server start.v0.5.7: Startup garbage collection for the MCP hook's
_completion_state.json. The file accumulated one entry perconversation_idever seen with no cleanup, driving I/O amplification on every write and leaving stuckscored_this_turn=Trueflags that could poison the cross-session scoring fallback. New_cleanup_completion_statepass drops entries older than 30 days or with no matching transcript, enforces a 500-entry hard ceiling, and writes atomically. JSONL transcript TTL bumped 7 → 30 days to stay in lockstep with the state-file TTL. Ships paired with Roampal Desktop v0.3.3.v0.5.6: Hardening release — closes remaining coverage gaps from the v0.5.5.x verification audit. Phantom sweep after archived cleanup, auto-cleanup under capacity pressure, dedup observability, hardened delete permissions, archive-then-add cycle tests, sidecar prompt alignment with benchmark, async scoring queue (per-session deferred retry), MCP tool definition quality rewrite (TDQS), OpenCode Go auto-detect in sidecar setup wizard, and user name extraction fix.
v0.5.5.2: Hotfix — Windows plugin install now verifies copy succeeded (post-copy size check + manual read/write fallback for OneDrive/antivirus interference). Also installs to
%APPDATA%\opencode\pluginsas fallback since some Electron apps resolve config paths differently on Windows. Fixes remaining cases of issue #11 whereroampal init --forcereported success but the plugin was empty or in the wrong directory.v0.5.5.1: Hotfix — OpenCode Desktop now correctly switches profiles when you switch projects in the UI (issue #10). Plugin reads the active session's
directoryviaclient.session.get()instead of caching the profile at module load, so a singleton plugin across a multi-project workspace still hits the right profile per message. Also:roampal init --forceactually overwrites the OpenCode plugin file now (issue #11), with clearer errors when Desktop holds a file lock.v0.5.5: Soft-delete for memory_bank — ChromaDB hard delete doesn't actually remove vectors from HNSW, causing phantom dedup matches that block new memories after GUI deletion. Replaced with
status=archivedmetadata update plus status filter on all query/dedup paths. Also: scoring mutex → async queue (eliminates dropped requests), sidecar summary contamination fix (delimiter fencing).v0.5.4: Profile binding is now per-request, not per-process. Every client (MCP server, OpenCode plugin, Python hooks for Claude Code / Cursor) sends an
X-Roampal-Profileheader so a single FastAPI server can cleanly serve multiple profiles simultaneously. Fixes issue #7 where OpenCode Desktop's per-projectROAMPAL_PROFILEinopencode.jsonwas ignored because the singleton FastAPI bound the profile once at startup.v0.5.3: Sidecar scoring now requires explicit configuration (no automatic fallback to Zen or localhost). Small local models (qwen2.5:3b, etc.) that return bare JSON arrays instead of OpenAI-shaped responses are handled transparently via server-side shape tolerance.
How It Works
When you type a message, Roampal automatically injects relevant context before your AI sees it:
You type:
fix the auth bugYour AI sees:
═══ KNOWN CONTEXT ═══
• JWT refresh pattern fixed auth loop [id:patterns_a1b2] (3d, 90% proven, patterns)
• User prefers: never stage git changes [id:mb_c3d4] (memory_bank)
═══ END CONTEXT ═══
fix the auth bugNo manual calls. No workflow changes. It just works.
The Loop
You type a message
Roampal injects relevant context automatically (hooks in Claude Code, plugin in OpenCode)
AI responds with full awareness of your history, preferences, and what worked before
Outcome scored — good advice gets promoted, bad advice gets demoted
Repeat — the system gets smarter every exchange
Five Memory Collections
Collection | Purpose | Lifetime |
| Current session context | 24h — promotes if useful, deleted otherwise |
| Past conversations | 30 days, outcome-scored |
| Proven solutions | Persistent while useful, promoted from history |
| Identity, preferences, goals | Permanent |
| Uploaded reference docs | Permanent |
Commands
roampal init # Auto-detect and configure installed tools
roampal init --claude-code # Configure Claude Code explicitly
roampal init --opencode # Configure OpenCode explicitly
roampal init --no-input # Non-interactive setup (CI/scripts)
roampal start # Start the HTTP server manually
roampal stop # Stop the HTTP server
roampal status # Check if server is running
roampal status --json # Machine-readable status (for scripting)
roampal stats # View memory statistics
roampal stats --json # Machine-readable statistics (for scripting)
roampal doctor # Diagnose installation issues
roampal summarize # Summarize long memories (retroactive cleanup)
roampal score # Score the last exchange (manual/testing)
roampal context # Output recent exchange context
roampal ingest <file> # Add documents to books collection
roampal books # List all ingested books
roampal remove <title> # Remove a book by title
roampal sidecar status # Check scoring model configuration (OpenCode)
roampal sidecar setup # Configure scoring model (OpenCode)
roampal sidecar test # Test scoring model response format (OpenCode)
roampal retag # Re-extract tags on memories using sidecar LLM
roampal sidecar disable # Disable scoring (removes config, retrieval still works)
# Sidecar scope flags (v0.5.3+) — OpenCode merges project-local over user-global config:
roampal sidecar setup --scope user # Write only to user-global config (~/.config/opencode/)
roampal sidecar setup --scope project # Write only to project-local opencode.json in cwd ancestry
roampal sidecar setup # Auto-detects: uses project-local if shadow exists, otherwise user-global
# Sidecar scope flags for disable (v0.5.3+):
roampal sidecar disable --scope user # Clear only from user-global config
roampal sidecar disable --scope project # Clear only from project-local opencode.json
roampal sidecar disable # Auto-detects scope same as setup
# Named memory profiles (v0.5.1) — isolate memory per project, per client, etc.
roampal profile list # List registered profiles
roampal profile show # Show active profile and its path
roampal profile create <name> # Create auto-located profile
roampal profile register <name> --path <dir> # Register an existing directory
roampal profile use <name> # Persist as user-global default
roampal profile unuse # Clear persistence
roampal profile switch <name> # Persist + kill running server
roampal profile delete <name> # Remove from registry
roampal start --profile <name> # One-off launch on a profileNamed Memory Profiles (v0.5.1)
Run separate memory stores for different contexts — per project, per client (Claude Code vs OpenCode), work vs home. Profiles are managed entirely through the CLI; no config files to hand-edit.
roampal profile create work # auto-located at <appdata>/Roampal/data/work/
roampal profile switch work # persist + kill running server
# next MCP tool call spawns a fresh server on 'work'Register an existing directory as a profile (no data migration):
roampal profile register project-a --path /existing/custom/pathPrecedence (highest wins):
--profile <name>flagROAMPAL_PROFILE=<name>env var (set per-project inopencode.jsonor.claude.jsonenv: {})roampal profile use <name>persisted default"default"fallback
MCP Tools
Your AI gets these memory tools:
Tool | Description | Platforms |
| Deep search across all collections | Both |
| Store permanent facts (identity, preferences, goals) | Both |
| Correct or update existing memories | Both |
| Remove outdated info | Both |
| Score previous exchange outcomes | Claude Code |
| Store key takeaways from significant exchanges | Both |
How scoring works: Claude Code's hooks prompt the main LLM to call
score_memoriesevery turn. OpenCode uses an independent sidecar that scores silently in the background — the model never sees a scoring prompt andscore_memoriesis not registered as a tool. If the sidecar is unavailable, a warning prompts the user to runroampal sidecar setup. Choose your scoring model duringroampal initor viaroampal sidecar setup.
How Roampal Compares
Feature | Roampal Core | Claude Code built-in (CLAUDE.md / auto memory) | OpenCode built-in |
Learns from outcomes | Yes — bad advice demoted, good advice promoted | No | No |
Semantic retrieval | Yes — TagCascade + cross-encoder reranking | No — files loaded in full, no search | No memory system |
Context injection | Automatic — relevant memories per query | Full CLAUDE.md every session, auto memory on demand | None |
Atomic fact extraction | Yes — summaries + facts, two-lane retrieval | No — saves what Claude decides is useful | No |
Works across projects | Yes — shared memory across all projects | Per-project only (per git repo) | No memory |
Scales with history | Yes — 5 collections, promotion/demotion/decay | CLAUDE.md unbounded, auto memory first 200 lines | No memory |
Fully local / private | Yes — ChromaDB on your machine | Yes | Yes |
┌─────────────────────────────────────────────────────────┐
│ pip install roampal && roampal init │
│ Claude Code: hooks + MCP → ~/.claude/ │
│ OpenCode: plugin + MCP → ~/.config/opencode/ │
└─────────────────────────────────────────────────────────┘
│
▼
┌─────────────────────────────────────────────────────────┐
│ HTTP Hook Server (port 27182) │
│ Auto-started on first use, self-heals on failure │
│ Manual control: roampal start / roampal stop │
└─────────────────────────────────────────────────────────┘
│
▼
┌─────────────────────────────────────────────────────────┐
│ User types message │
│ → Hook/plugin calls HTTP server for context │
│ → AI sees relevant memories, responds │
│ → Exchange stored, scored (hooks or sidecar) │
└─────────────────────────────────────────────────────────┘
│
▼
┌─────────────────────────────────────────────────────────┐
│ Single-Writer Backend │
│ FastAPI → UnifiedMemorySystem → ChromaDB │
│ All clients share one server, isolated by session │
└─────────────────────────────────────────────────────────┘See dev/docs/ for full technical details.
Requirements
Python 3.10+
One of: Claude Code or OpenCode
Platforms: Windows, macOS, Linux (primarily developed and tested on Windows)
RAM: ~500MB available (cross-encoder reranker + embeddings + ChromaDB); first-run migration after an upgrade adds no meaningful spike
Disk: ~500MB for models (multilingual embedding + reranker, downloaded automatically on first use)
CPU: Any modern x86-64 processor with AVX2 (Intel Haswell 2013+ / AMD Excavator 2015+)
GPU: Not required — all inference runs on CPU via ONNX Runtime
Troubleshooting
Restart Claude Code (hooks load on startup)
Check HTTP server:
curl http://127.0.0.1:27182/api/health
Verify
~/.claude.jsonhas theroampal-coreMCP entry with correct Python pathCheck Claude Code output panel for MCP errors
Make sure you ran
roampal init --opencodeCheck that the server auto-started:
curl http://127.0.0.1:27182/api/healthIf not, start it manually:
roampal start
This is expected. Roampal has self-healing -- if the HTTP server stops responding, it is automatically restarted and retried.
Still stuck? Ask your AI for help — it can read logs and debug Roampal issues directly.
Support
Roampal Core is completely free and open source.
Support development: roampal.gumroad.com
Feature ideas & feedback: Discord
Bug reports: GitHub Issues
Need help with AI memory? Reach out: roampal@protonmail.com | LinkedIn
License
Available Tools
6 toolsadd_to_memory_bankA
Store permanent facts about the user
| Name | Required | Description | Default |
|---|---|---|---|
| content | Yes | Memory content |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description bears full weight. It mentions 'permanent' but doesn't disclose behavior on duplicates, overwriting, or expected side effects.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Single sentence efficiently communicates purpose, though it could be expanded with behavioral or usage notes without becoming verbose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple memory-add tool with no output schema, the description is minimally adequate but lacks details on idempotency, memory limits, or confirmation of storage.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Input schema coverage is 100% with a clear description for the sole parameter 'content'. The description adds context but no additional meaning beyond schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Store permanent facts about the user' uses a specific verb and resource, clearly distinguishing this tool from siblings like delete, update, or search.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for storing permanent user info but provides no explicit guidance on when to use vs alternative tools like update_memory or delete_memory.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
delete_memoryC
Delete a memory by ID
| Name | Required | Description | Default |
|---|---|---|---|
| memory_id | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided; description only states the action but lacks details about permanence of deletion, authorization, or side effects.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Very brief, which aids quick understanding but at the expense of completeness.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Lacks information on return values, error handling, or any constraints; insufficient given no annotations or output schema.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema has 0% description coverage; the description only implies the memory_id parameter's role without adding semantic meaning beyond the type.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action 'delete' and the resource 'memory' along with the identifier method 'by ID', distinguishing it from siblings like add, search, update.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool versus alternatives or any prerequisites for deletion.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
record_responseA
Store key takeaways from significant exchanges. NOT for permanent preferences or standing rules — use add_to_memory_bank for those.
| Name | Required | Description | Default |
|---|---|---|---|
| key_takeaway | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. It describes a write operation but does not clarify whether it overwrites, appends, or requires authentication. Adequate but lacks depth.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences: first states purpose, second provides usage guidance. No wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the single parameter, no output schema, and simple purpose, the description is mostly complete. Could be improved by noting whether each call creates a new entry or updates an existing one.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema has 0% description coverage and the description adds no additional meaning to the parameter key_takeaway beyond its name. For a single parameter, this is a missed opportunity to specify format or constraints.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Clearly states the tool stores key takeaways from significant exchanges. Explicitly distinguishes from sibling add_to_memory_bank by stating what not to use it for.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides explicit negative guidance ('NOT for permanent preferences or standing rules') and directs to an alternative tool (add_to_memory_bank).
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
score_memoriesD
Score previous exchange outcomes
| Name | Required | Description | Default |
|---|---|---|---|
| memory_scores | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must disclose behavior. 'Score previous exchange outcomes' does not indicate whether the tool is read-only, mutates data, requires authorization, or has side effects. It offers virtually no behavioral insight.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely short (3 words), which under both conciseness and structure. It provides no additional information, making it more under-specified than concise. The brevity harms usefulness.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has a nested object parameter and no output schema or annotations, the description should compensate with richer context. It fails to explain the scoring mechanism, expected input format, or return value, leaving the agent with almost no useful information.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has one parameter 'memory_scores' (object) with 0% description coverage. The description does not explain what this parameter represents, what keys or values it expects, or any constraints. It adds no semantic value beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Score previous exchange outcomes' provides a verb and resource, but the meaning of 'score' is ambiguous—it could mean evaluate, assign a rating, or keep track. It does not differentiate from siblings like 'record_response' or 'add_to_memory_bank', which have clearer purposes.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is given on when to use this tool versus alternatives. There is no mention of context, prerequisites, or scenarios where this tool is appropriate or inappropriate.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
search_memoryB
Search across memory collections for relevant context
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | Search query |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, and the description does not disclose behavioral traits such as whether the search is semantic or exact, how many results are returned, or any limitations. The schema provides the parameter but no extra context beyond the description.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence that is concise and front-loaded with the action. While efficient, it could be slightly expanded to include return behavior without losing conciseness.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the simple input schema (one string parameter) and no output schema, the description is somewhat complete but lacks details on return format, ranking, or pagination. It is adequate for a basic search tool but could be improved.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the baseline is 3. The tool description does not add any additional meaning beyond what the schema already provides for the 'query' parameter.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Search') and the resource ('memory collections'), distinguishing it from sibling tools that involve adding, deleting, recording, scoring, or updating memories. It is specific enough to indicate this is the retrieval tool.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description does not provide explicit guidance on when to use this tool versus alternatives, but the verb 'Search' and the sibling tool names imply this is the appropriate choice for finding relevant context. No exclusions or conditions are mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
update_memoryC
Update an existing memory
| Name | Required | Description | Default |
|---|---|---|---|
| content | Yes | ||
| memory_id | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, and the description only says 'Update', which implies a write operation. It does not disclose behavioral traits such as whether existing content is overwritten or if there are any prerequisites.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise with a single sentence, but it is too brief and could be more informative without additional length.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the lack of annotations, output schema, and minimal description, the tool definition is incomplete. It does not explain return values, side effects, or prerequisites.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, and the description does not explain the meaning of memory_id or content, leaving the AI agent without essential context for parameter use.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'Update' and the resource 'existing memory', distinguishing it from siblings like delete_memory, search_memory, etc. However, it lacks specificity about what 'memory' entails.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool vs. alternatives like add_to_memory_bank. No exclusions or context provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
No tool schema history has been recorded yet.
TDQS
Each tool targets a distinct operation: permanent facts, exchange takeaways, scoring, search, update, and delete. Descriptions clearly differentiate purposes, leaving no ambiguity for an agent.
All tool names follow a consistent verb_noun or verb_preposition_noun pattern in snake_case, ensuring predictability and clarity.
With 6 tools covering the core operations of a memory system, the count is well-scoped—neither too few nor excessive for the domain.
The set provides complete CRUD (create via add_to_memory_bank and record_response, read via search_memory, update via update_memory, delete via delete_memory) plus an additional scoring capability, leaving no obvious gaps.
Maintenance
Related MCP Connectors
Persistent memory and cross-session learning for AI coding assistants (hosted remote MCP).
Adaptive plan/build/review cycles for AI coding assistants, persisted across sessions.
Shared debugging memory for AI coding agents
Persistent cross-session memory shared by Codex, Claude Code, ChatGPT, and other AI agents.
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- AlicenseNot gradedqualityBmaintenancePersistent, cross-tool memory for AI coding assistants, enabling context retention across sessions, tools, and devices with a three-tier memory model and hybrid search.1,09114MIT
- AlicenseAqualityCmaintenanceLong-term memory for AI coding assistants. Remembers context once and recalls it across sessions.721MIT
- AlicenseNot gradedqualityCmaintenancePersistent cross-session memory for AI coding assistants, automatically capturing and injecting context across sessions via MCP tools.1311AGPL 3.0
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