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Glama

TeamShared

by io.github.xhad

Memory Remember

memory_remember

Write a durable memory into the caller's org.

fact / preference / note / outreach -> semantic pillar. event -> episodic. procedure / skill -> rejected; use memory_procedure_set / memory_skill_set. Routed through the guarded ingestion pipeline (PII, injection screening, near-dupe merge / contradiction supersede) under RLS. When repo / github are given the memory is tagged repo:<slug> / github:<owner>/<repo>.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
kindNofact, preference, event, note, or outreach (not procedure/skill)note
repoNoWorkspace slug of the repo this memory belongs to (e.g. the slug used for memory_state). For code/repo-specific work, pass your current workspace slug so the memory is scoped to this repo (stored as a 'repo:<slug>' tag) and ranks higher when recalled from the same repo. Omit for cross-cutting memories.
tagsNoOptional free-form tags
agentNoOverride agent identity (defaults to bearer-token identity)
githubNoGitHub repository as owner/repo (e.g. xhad/teamshared). Stored as a 'github:<owner>/<repo>' tag for cross-machine association; use with or instead of workspace repo= when the same GitHub repo is checked out at different paths.
contentYesFree-form text to remember
subjectNoOptional subject/entity this memory is about

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Changed2 schema fields changed
    • changedInput schema / properties / kind / description
      Previous value: -"fact, preference, event, or note (not procedure/skill)"New value: +"fact, preference, event, note, or outreach (not procedure/skill)"
    • changedInput schema / properties / kind / enum
      Previous value: -[
-  "fact",
-  "preference",
-  "event",
-  "note",
-  "procedure",
-  "skill"
-]New value: +[
+  "fact",
+  "preference",
+  "event",
+  "note",
+  "procedure",
+  "skill",
+  "outreach"
+]
  2. First observed

TDQS

A4.9/5.0
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full behavioral burden, and it does so well. It discloses the guarded ingestion pipeline (PII, injection screening, near-dupe merge / contradiction supersede), RLS enforcement, durability, and sticky behavior for repo/github tags. This goes well beyond a bare write operation.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is compact and scannable, front-loading the core purpose before routing rules and pipeline behavior. Every sentence earns its place; code-formatted keywords and arrow notation condense a lot of information without fluff.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The tool has 7 parameters and an output schema, but the schema covers all parameters and the description covers what the schema cannot: when to use it, which kinds are rejected, behavioral pipeline details, and tag semantics. An agent has enough information to call it correctly and avoid the common procedure/skill misuse.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so the baseline is 3. The description adds meaningful semantics beyond the schema by mapping kind values to semantic pillars (fact/preference/note/outreach vs episodic event) and by explaining how repo and github parameters produce specific tags that affect recall ranking. This is clear added value without needing to restate every parameter.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description opens with a specific verb and resource: 'Write a durable memory into the caller's org.' It also clarifies what kinds are accepted (fact, preference, event, note, outreach) and explicitly routes procedures/skills to separate tools, distinguishing it from memory_procedure_set and memory_skill_set siblings.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly states which kind values are rejected ('procedure' / 'skill' -> rejected) and names the alternatives to use instead ('use memory_procedure_set / memory_skill_set'). It also gives contextual guidance for repo and github parameters, telling the agent when to scope memories to a repo or GitHub association.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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TDQS

B3.4/5.0
Disambiguation4/5

With 104 tools across many domains (memory, work, projects, files, agents, context, strategic, ontology), the use of clear prefixes (memory_, work_, project_, file_, agent_run_, context_) makes most tools distinct. However, there are some potential confusions between memory_session_* vs memory_state_*, and memory_recall vs memory_think vs memory_assemble_context, though descriptions clarify their specific purposes. Aliases like memory_playbook_get for memory_procedure_get are explicit and reduce ambiguity.

Naming Consistency5/5

Tool names follow a highly consistent pattern: prefix_domain_action (e.g., file_create, work_update, memory_recall, agent_run_start). All use snake_case, with verbs consistently placed after the domain prefix. Even less common tools like account_brief and attention_snapshot fit the overall naming scheme, making the set predictable and easy to navigate.

Tool Count3/5

At 104 tools, this is an exceptionally large surface area, far exceeding the 25+ threshold that feels heavy. However, the server covers an extensive domain (organizational memory, work management, project tracking, file sharing, agent orchestration, and strategic planning), which justifies a large count. Still, the sheer number may overwhelm agents, and some tools could be consolidated (e.g., many memory_session_* and memory_state_* variants).

Completeness4/5

The tool surface is remarkably complete for its stated purpose, covering CRUD operations for files, work items, projects, and memory, plus lifecycle management for agents, sessions, and strategic plans. Minor gaps exist (e.g., no direct memory_item_get by ID, no section removal in projects), but agents can work around these using existing tools like memory_recall or work_create with parent_id. Overall, the set minimizes dead ends.

Resources