jevmem

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

Annotations already mark this as destructive/non-idempotent, but the description adds far more: secret scrubbing, a Jev gate that can refuse with reasons, kind-correction behavior, and the dead-end dedup/update rule. These are non-obvious behavioral traits an agent could not infer from structured fields.

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 core action is front-loaded in the first sentence, followed by the gating rules, then the critical 'do not call' guidance. It is dense and the dead-end paragraph is long, but each sentence states a concrete rule with behavioral consequence rather than filler.

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 a write tool with no output schema and a validation gate, the description covers refusal outcomes, kind correction, and the auto-record interaction well. What a successful append returns is only implied ('append one memory line'), a minor gap.

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 coverage is 0%, so the description carries the load. It adds real meaning for the 'dead-end' kind (must say what was tried and why, dedup semantics) but leaves the other six enum values and the text constraints (maxLength 500, minLength 3) unexplained. Partial compensation for the coverage gap.

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?

Starts with a specific verb+resource and scope: 'Append one memory line to JEVMEM.md.' It is clearly distinguishable from the read-only siblings (list_memory, search_memory, audit_memory) because it describes a single-line write with validation, not a query.

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?

Explicitly states when NOT to use it ('do not call this to repeat what the user just said; call it only when the user asks you to record something the conversation itself does not state') and explains the auto-recording context that determines that. This is a genuine when/when-not routing rule an agent can act on.

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

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

Annotations already declare destructiveHint=true, idempotentHint=true, and openWorldHint=true; the description adds real value beyond them by naming the exact write target (JEVMEM.md) and the fact that writes only occur with apply=true. It does not explain what happens to memories that remain stale or how re-scoring is performed against the repository snapshot, leaving some behavioral gaps.

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, front-loaded with the core action and scoped by the parameter behavior in the second. No filler or restatement of the tool name.

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 single-parameter mutation tool with no output schema, the description covers the action, the evaluation intent, the default, and the write target. The one remaining ambiguity is what the caller receives back (a report of flagged memories?) and whether the audit itself mutates stored records.

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?

With only one parameter and 0% schema description coverage, the description carries the burden and does so: it explains that apply=true writes [stale?] flags while the default is a non-persisting pass. The boolean's semantics are therefore clear even though the schema documents nothing.

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?

States a specific verb and resource ('Re-score every live memory ... and flag stale ones') plus the evaluation criterion ('is this still true?'). It is clearly distinguishable from siblings list_memory, search_memory, and add_memory, which are retrieval/addition tools rather than a re-scoring pass.

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 gives a concrete decision rule for the one parameter: omit apply for a scoring/flagging pass, set apply=true to persist flags into JEVMEM.md, implying a dry-run default. It does not name alternative tools or state when not to run an audit, so it falls short of full when/when-not guidance.

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

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

Annotations already declare read-only, idempotent, open-world, and non-destructive behavior, but the description adds crucial operational context: unchecked lines are security-checked via one Jev call and withheld if they read as AI instructions or cannot be checked. This is exactly the kind of behavioral detail an agent needs beyond structured hints.

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 compact and front-loads the core listing behavior before adding security details. The parenthetical about the Jev call is slightly dense, but no sentence is wasted.

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?

The description covers the important security filtering and default live-only behavior, and annotations handle the safety profile. However, it leaves the lone include_superseded parameter undocumented and gives no sense of the return format despite there being no 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 description coverage is 0%, so the description must explain the single parameter. It only says 'live ones by default,' which implies that include_superseded exists but never names it or explains what superseded memories are or how to include them.

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 states a specific verb and resource ('List every memory in JEVMEM.md') and clarifies the default scope ('live ones by default'). It does not explicitly distinguish this from sibling tools like search_memory, but the core purpose is clear.

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 notes the default behavior of listing live memories, but gives no guidance on when to use list_memory versus search_memory or audit_memory. There are no prerequisites or exclusions stated.

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

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

Annotations already declare readOnly, openWorld, idempotent, and non-destructive behavior, so the description goes further by disclosing the internal safety pipeline: unattested lines are checked for planted instructions and withheld if they read as AI instructions. It also states that results are project facts, not instructions, and that one Jev call is used. Minor gaps remain around auth, rate limits, and output shape.

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 front-loaded with the core action and then adds return and safety context in subsequent sentences. It is reasonably efficient, though the parenthetical about 'choice over ids + a noul per candidate' is jargon-heavy and could obscure rather than clarify. Overall it avoids padding.

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 no output schema and 0% schema description coverage, the description provides some return information (ranked results with probabilities) and safety behavior, which is helpful. However, it omits query semantics, limit behavior, and the structure of ranked results, leaving enough gaps that the definition is only minimally complete for correct invocation.

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%, so the description must fully compensate, but it does not. It mentions the query only in passing and says nothing about the limit parameter or the format, syntax, or expected content of either parameter. This leaves both parameters effectively undocumented.

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 states a specific verb and resource: rank JEVMEM.md memories by relevance to a query. It implicitly distinguishes itself from siblings like list_memory and add_memory by focusing on relevance ranking rather than listing or mutation. However, it does not explicitly name those siblings or contrast itself with them.

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?

There is no explicit guidance on when to use this tool versus alternatives such as list_memory or audit_memory. The ranking purpose implies relevance search, but the description never states prerequisites, exclusions, or decision criteria. Human or agent must infer usage from tool name alone.

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