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marerem

longmem

save_solution

Store a problem-solution pair for reuse across projects. Enter the problem, solution, and category to build a searchable knowledge base.

Instructions

Save a problem/solution pair to the cross-project memory.

Call this after successfully solving a problem so future sessions — in any project — can find and reuse the solution. Returns the entry ID which can be passed to add_edge_case later.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
problemYesClear description of the problem that was solved.
solutionYesThe solution, including code snippets, commands, or steps. Be specific — this will be reused verbatim in future projects.
categoryYesProblem domain. One of: ci_cd, containers, infrastructure, cloud, networking, observability, auth_security, data_pipeline, ml_training, model_serving, experiment_tracking, llm_rag, llm_api, vector_db, agents, database, api, async_concurrency, dependencies, performance, testing, architecture, other
projectNoRepository or workspace name this was solved in.
tagsNoKeywords for filtering: library names, tools, error types. E.g. ['airflow', 'dag', 'python', 'skip'].
languageNoProgramming language, e.g. 'python'.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior3/5

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

No annotations provided, so description carries burden. Describes saving to memory and returning an entry ID. Lacks details on whether duplicates are overwritten or if confirmation is needed. Adequate for a simple save 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?

Three sentences, no filler. Front-loaded with purpose, then usage guidance, then output hint. Every sentence earns its place.

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

Completeness4/5

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

Covers purpose, when to use, output usage. With output schema present, return values are covered. Could mention persistence across projects, but it's implied by 'cross-project memory'. Lacks explicit note on no side effects.

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 baseline is 3. Description adds value by advising to be specific for reuse in the solution parameter. Also mentions category list implicitly. Adds meaning beyond schema.

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?

Clearly states it saves a problem/solution pair to cross-project memory. Specifies use case (after solving) and return value (entry ID). Distinguishes from sibling tools like search_similar and confirm_solution.

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

Usage Guidelines4/5

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

Explicitly says when to call (after successfully solving a problem) and hints at follow-up with add_edge_case. Could be improved by noting when not to use, but the guidance is clear enough.

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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