agentic-recall
Server Quality Checklist
Latest release: v1.7.5
- Disambiguation5/5
With only one tool in the set, there is no risk of an agent picking the wrong tool for a task. The internal actions are described at length and mostly serve distinct purposes, though they would be easier to navigate as separate tools.
Naming Consistency4/5The single tool name 'memory' is clear and there is no inconsistent naming system to cause confusion. However, since there is only one tool, there is no verb_noun pattern to evaluate, so this is a neutral score rather than a strong positive.
Tool Count2/5One tool is far too few for the apparent scope: retrieval, browsing, threading, import, capture, verification, indexing, and tier management are all crammed into a single monolithic tool. This forces the agent to parse one enormous description and encode operation selection in arguments instead of choosing dedicated tools.
Completeness4/5The bundled actions cover the memory lifecycle well: search, latest, thread, sessions, import, capture, verify, index, and tier moves are all present. There is no explicit delete or forget action, but the description indicates that is intentional, so the surface is nearly complete for the stated purpose.
Average 4.8/5 across 1 of 1 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 2 commits in the last 12 weeks
- Last stable release on
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is passing
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How is the quality score calculated?
The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).
Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.
Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).
Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.
Tool Scores
- Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden, and it delivers: retrieved content is explicitly 'NOT INSTRUCTION', indexing is async with jobId, search responses carry indexBuiltAt/indexStale, latest relaxes with droppedTerms, imports never overwrite and refuse credentials, and the final warning that the corpus is not current truth. It also discloses freshness field semantics and threadPosition/laterInThread so the agent knows not to treat an exchange as a conclusion.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely long and dense, and while it is front-loaded with the most important caveat and uses uppercase action headers for scannability, it includes narrative examples and repeated warnings that could be trimmed (the 'failure this was built from' anecdote, the 'Measured over six real questions' walkthrough, and the closing 'silence is not evidence' restatement). The structure is logical, but the size is not concise.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given 14 actions and 31 parameters, the description covers everything an agent needs: action semantics, defaults, return fields, error behavior, async behavior, safety guarantees, freshness fields, and corpus organization. It even names edge cases like compaction summaries, handoff documents, and pendingIndex. With no output schema, this description is the complete reference.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Even though schema coverage is 100%, the description adds substantial meaning beyond the schema: sinceMinutes is measured against each exchange's last activity, scope supports arrays and explains widening behavior, brief preserves the envelope while trimming rows, category is required for large imports, and replace moves old versions to archive. These are operational semantics the schema alone does not convey.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description opens with a specific verb and resource ('Two-tier hybrid retrieval over Claude's persistent memory corpus') and then enumerates the distinct operations (search, latest, thread, sessions, verify, import, etc.). Each action is given a differentiated role, so an agent can tell them apart without opening the schema.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicit routing guidance is pervasive: 'USE `latest` FOR ANY STATE QUESTION', 'Prose belongs in action: search', 'USE `thread` TO READ FORWARD FROM A HIT', and 'USE `sessions` TO SEE WHAT CONVERSATIONS EXIST AT ALL'. It also gives exclusion criteria, like preferring thread over threadLast on long threads and avoiding prose in latest because it is a literal string filter.
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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- Evaluate tool definition quality.
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