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hermes-labs-ai

io.github.hermes-labs-ai/fidelis-memory

Official

fidelis_orient

Re-establishes context for prior work by returning an evidence-bound orientation packet when a turn references known projects, decisions, or earlier work, even if not phrased as a question.

Instructions

Context-sensitive re-entry for prior work. Call when a turn mentions a known project, decision, earlier work, maintenance, comparison, or possible reuse—even when the turn is not phrased as a question. Returns an evidence-bound orientation packet or explicitly abstains.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNo
entityNoOptional exact known project or concept name
utteranceYesCurrent user turn
recent_turnsNoUp to four recent turns for referent resolution

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.7/5.0
Behavior3/5

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

With no annotations, the description carries the behavioral disclosure burden. It does disclose a meaningful behavioral trait: it returns an evidence-bound orientation packet or explicitly abstains. However, it does not clarify what the packet contains, whether the operation is read-only, or what triggers abstention beyond the listed topics.

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 tightly packed sentences with no filler. The purpose, call conditions, and output/abstention behavior are compactly and effectively ordered; every sentence earns its place.

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

Completeness3/5

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

Without annotations or an output schema, the description gives a clear high-level contract but leaves the orientation packet contents and the role of optional parameters under-specified. It is enough to guide an agent toward the tool, but not enough to fully set expectations about what comes back.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 75%, so the schema already documents utterance, entity, and recent_turns. The description adds usage context for the utterance but does not clarify the limit parameter or define the evidence-bound packet contents. It neither contradicts nor meaningfully augments the schema, leaving the schema to carry most of the weight.

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?

The description states a specific function: context-sensitive re-entry into prior work, and lists concrete trigger signals (known project, decision, earlier work, maintenance, comparison, reuse). It does not explicitly distinguish itself from sibling tools fidelis_recall, fidelis_query, or fidelis_health, so it falls just short of a 5.

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?

It gives explicit when-to-call guidance, covering a wide range of trigger phrases and noting that it applies even when the turn is not phrased as a question. It does not name alternatives or state when not to use this tool, so it lacks the explicit exclusion that would make a 5.

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