haki

haki_context

Recall the project's memory (decisions, conventions, preferences) relevant to a task. Call BEFORE planning or editing code. Returns a ready-to-inject block with dates and sources, the trace_id (inspectable via haki_inspect), and an explicit status ("ok"/"degraded"/"failed") — never treat a degraded or failed response as "no known facts".

If the block does not hold what was needed, call this tool again with
the SAME query and `exclude_ids` set to the ids already received — it
serves the next page of the same ranked list. Do NOT rewrite the query
with what was just read: measured on this project's bench, that finds
the missing turn less often than asking unchanged.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`query`	Yes
`exclude_ids`	No
`budget_tokens`	No

Output Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

Schema Changelog

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

First observedSep 8, 2026

TDQS

A4.6/5.0

Behavior5/5

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

No annotations are provided, so the description carries full behavioral disclosure. It explains the return structure (block with dates, sources, trace_id, status), the meaning of status values, and warns against misinterpreting degraded/failed as 'no known facts'. It also discloses pagination behavior via exclude_ids.

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 front-loaded with the core purpose and usage instruction, then adds behavioral warnings and pagination details. Every sentence serves a purpose, with no fluff. It is appropriately sized for the complexity.

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?

Given the output schema exists and the description explains the status field, pagination, and usage context, the description is complete for an agent to call the tool correctly. It covers what to do on failure and how to interpret results.

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 0%, so the description must explain parameters. It clearly explains query (implicitly) and exclude_ids (pagination), but budget_tokens is not mentioned. However, budget_tokens is an optional integer with a default, and its purpose is reasonably inferable. The description adds significant value for the key parameters.

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

Purpose4/5

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

States a specific verb and resource: recalls project memory (decisions, conventions, preferences) relevant to a task. It is clear about the function, though it does not explicitly differentiate from sibling tools like haki_capture or haki_forget. However, the purpose is unambiguous.

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?

Explicitly instructs to call BEFORE planning or editing code, and provides detailed guidance on failure handling: call again with the same query and exclude_ids, and warns against rewriting the query. This is strong, actionable usage guidance with clear conditions.

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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