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invinoveritas

by com.babyblueviper

memory_store

Idempotent

Store persistent long-term memory for this agent (cross-session, cross-client, cross-model, as long as you authenticate with the same Bearer key) — works with any MCP client (Claude, Cursor, Cline, etc.). Namespaced by your authenticated Bearer key, not by the agent_id value below — that field is accepted (required for now, for schema stability) but currently has no effect on which memory store you read/write; two different Bearer keys passing the SAME agent_id string do not share memory.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
keyYesMemory key (e.g. 'architecture_decisions', 'open_questions', 'last_trade_plan')
valueYesThe data to persist (text, JSON string, or structured notes)
agent_idYesAccepted but not currently used to partition storage — memory is namespaced by your Bearer key. Pass any stable string (e.g. 'my-repo-agent').

TDQS

A4.4/5.0
Behavior5/5

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

The description goes well beyond the annotations by revealing critical naming behavior: memory is namespaced by Bearer key, not by agent_id, and agent_id is a no-op placeholder. It also clarifies durability across sessions, clients, and model boundaries. The annotations (idempotentHint, readOnlyHint=false) are consistent and supplemented with meaningful operational context.

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

Conciseness4/5

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

The description is a single dense paragraph that front-loads the core purpose and then explains the namespacing nuance. It is information-dense and every clause contributes, though it could be structurally split into two sentences for readability. Overall, it is concise given the complexity it covers.

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 description fully explains the tool's behavior including persistence scope, authentication requirements, and the agent_id no-op. With no output schema needed for a store operation, the description is complete for an AI agent to invoke it correctly. It also complements the sibling toolset by making the write semantics unambiguous.

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?

The schema already describes all three parameters at 100% coverage, including an example. The description adds extra semantic value by explicitly stating that agent_id has no effect on storage partitioning, which is not conveyed by the schema's phrasing alone. This clarifies a subtle but important usage detail.

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 begins with a specific verb and resource: 'Store persistent long-term memory for this agent'. It clearly distinguishes from sibling tools like memory_get, memory_list, memory_delete, and memory_search by indicating this is the write operation. The cross-session/cross-client details further refine the purpose.

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

Usage Guidelines3/5

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

The description provides contextual guidance about authentication (same Bearer key) and the agent_id caveat, which helps decide when to use the tool. However, it does not explicitly mention alternatives like memory_get or memory_search for retrieval, nor does it state any exclusions. Usage is implied rather than directly contrasted with siblings.

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

A3.6/5.0
Disambiguation4/5

Most tools have clearly distinct purposes, but there is potential confusion between 'decision' and 'reason', both offering advisory output. Also, 'review', 'witness', 'prove', and 'verify_proof' overlap in the proofs space, though descriptions differentiate them. Overall, an agent can disambiguate with careful reading.

Naming Consistency4/5

All tool names use lowercase and underscores (snake_case), which is consistent. However, the verbs vary: some are imperative (e.g., 'browse', 'execute'), while others are nouns (e.g., 'signals', 'ledger'), breaking a strict verb_noun pattern. Overall, the naming is readable and mostly predictable.

Tool Count2/5

With 30 tools, the surface is too large for a well-scoped server. Many functions could be separated (e.g., memory, workspace, feedback, marketplace). This excess makes it harder for an agent to navigate and select the right tool quickly.

Completeness3/5

The tool set covers core CRUD for memory and workspace, plus feedback, marketplace purchase, bounties, and verification. However, there is no tool to list or search marketplace listings, and workspace creation is only implicit via 'execute'. These gaps hinder fluid workflows.

Resources