get_causal_chainGet the causal chain graph for a specific decision, showing how decisions, actions, and supersessions relate.
| Name | Required | Description | Default |
|---|---|---|---|
| decision_id | Yes |
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
There are no annotations, so the description carries the burden of disclosing behavior. It reveals that the tool returns a graph of relationships, which is useful, but it does not mention potential errors, permissions, whether the decision must exist, or any side effects (though 'Get' implies a read-only operation).
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 a single, well-structured sentence that front-loads the main action and resource, then provides clarifying detail. Every word earns its place with no redundancy or unnecessary elaboration.
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 tool is simple (one parameter, output schema exists), and the description covers the core purpose and result type. However, it lacks usage guidance, edge-case behavior, and any note about when to choose this over related chain/audit tools, which is a gap given the large sibling set.
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?
The schema has one parameter, decision_id, with no description in the schema (0% coverage). The description ties it to 'a specific decision', which clarifies its purpose, but it does not provide format requirements or how to obtain the ID. The parameter is self-explanatory given the tool name, so the description adds minimal value beyond the obvious.
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 clearly specifies the verb 'Get' and the resource 'causal chain graph for a specific decision', with additional detail on what the graph shows (decisions, actions, supersessions). This distinguishes it from sibling tools like get_correction_chain or get_decision_context by focusing on the causal chain concept.
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 implies usage when you have a specific decision and need its causal chain, but it does not explicitly state when to use this tool versus alternatives like get_correction_chain or get_audit_trail. No exclusions or alternative recommendations are provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Several tools have overlapping purposes: check_claim and memory_claim_check are the same function with different tiers, get_freshness_report and memory_freshness_report are duplicates, get_correction_chain and compare_versions both trace history, and investigate_question subsumes many other retrieval tools. This will cause agent misselection.
Most tools follow a verb_noun pattern (get_constraints, declare_constraint, sync_pull, etc.). Exceptions like memory_claim_check, memory_freshness_report, session_debrief, and verify_before_acting deviate but are relatively few. Overall the naming is readable and mostly predictable.
75 tools is excessive for a memory server. Many are highly niche or direct duplicates (e.g., paid/free versions of the same function). The count far exceeds typical well-scoped servers and introduces cognitive overhead without proportional value.
The server covers a wide array of memory, constraint, decision, skill, and sync operations, which is impressive. However, there are notable gaps: no direct delete or edit for memory facts (only corrections/versioning), and no bulk enumeration of all stored items. The redundancy in retrieval tools also suggests an incomplete design.