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Warmth Engine Observatory

by com.warmthengine

get_topology

Retrieve the precomputed Coordination Topology — the structural lineage layer over the parent→child Coordination Connection graph (a DAG). Omit node_id for whole-graph network statistics (taxonomy-class counts, roots, multi-parent nodes, dominant roots by coordination reach, weakly-connected components). Pass node_id (e.g. "E34") for one node's structural metrics: generational depth (min/max/all-paths), coordination reach, directed betweenness, path diversity, fan-in/out, taxonomy class, component id. For the actual paths between nodes or up/down a lineage use traverse_coordination; for one event's value-chain stack use get_event_stack. Optional class / cc_type / min_reach filters return matching nodes. The full tier adds the held analyst layer (chain participation, curated orphan/sibling and named-feature sets).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
classNoFilter nodes by taxonomy class: root | internal | leaf | sibling_only | orphan
cc_typeNoFilter to nodes incident to a backbone (parent→child) edge of this type (1–7)
node_idNoNode id e.g. "E34"; omit for the whole-graph summary
min_reachNoFilter to nodes with coordination_reach >= this value

TDQS

A4.7/5.0
Behavior4/5

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

With no annotations, the description carries the full disclosure burden. It reveals that the tool returns a precomputed structural layer, which implies read-only access, and it details what each call mode returns. However, it does not mention data staleness from being precomputed or any other behavioral caveats such as performance or error semantics.

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 dense but well organized: it front-loads the resource, then covers the two call modes, alternatives, filters, and the extended tier. Every sentence contributes meaningful guidance, and there is no fluff or redundant repetition of the schema.

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?

Even without an output schema, the description enumerates the return categories for whole-graph and single-node calls, names the sibling tools for related operations, and explains the optional filters. This gives an agent enough context to invoke the tool correctly and interpret the result.

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 the baseline is 3, but the description adds semantics by linking node_id to per-node structural metrics and explaining that class, cc_type, and min_reach are filters that return matching nodes. It also clarifies that cc_type concerns backbone parent→child edge types.

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 opens with 'Retrieve the precomputed Coordination Topology,' identifying a specific verb and resource, and immediately distinguishes this tool from traverse_coordination and get_event_stack. This makes its role clear even among many sibling tools.

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?

It explicitly states when to use alternatives: use traverse_coordination for actual paths and get_event_stack for value-chain stacks. It also explains two invocation modes—omitting node_id for whole-graph stats vs passing node_id for per-node metrics—so an agent knows how to choose.

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

A4.6/5.0
Disambiguation5/5

Each tool targets a distinct data slice or operation. For example, `get_actors` retrieves full actor profiles while `query_scp` offers pivoting and comparison; `search_events` finds event IDs and `get_event` retrieves a full record; `get_threads` provides material links separate from `traverse_coordination` for lineage walks. There is no overlap in purpose.

Naming Consistency5/5

All tool names follow a consistent `verb_noun` pattern with underscore separators. Most use `get_` for retrieval, with `query_scp`, `search_events`, and `traverse_coordination` using different verbs that accurately reflect their distinct actions. The naming is predictable and intuitive.

Tool Count5/5

16 tools is well-scoped for the complexity of an observation platform that covers actors, blocs, events, connections, topology, vocabulary, statistics, and material links. Each tool earns its place by covering a necessary query pattern without unnecessary fragmentation.

Completeness5/5

The tool set provides comprehensive coverage of the domain: actor retrieval and querying (profiles, comparisons, watch), bloc membership, event discovery and full records, connection traversal and filtering, structural topology, material links, vocabulary lookups, and summary statistics. No obvious gaps in read access are present.

Resources