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Glama

synthesise

Consolidate many seed nodes into one durable memory node, linking back to each seed for provenance tracing. Use when scattered observations converge into one finding.

Instructions

Many-to-one consolidation — write one durable node from several seeds, with a derived_from edge to each so trace can walk back to them. Use when scattered observations have converged into a single finding; settle is the one-to-one version, which promotes a single node in place.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
fromYesSeed node names.
tierNoTier override. A CLOSED vocabulary, one of exactly these: `operational`, `archival`. Defaults from the type. (Not to be confused with the memory *layer* — `core`/`hot`/`warm`/`cold`/`frozen` — which `layer` sets.)
new_bodyYesBody for the synthesised node.
new_nameYesName for the synthesised node.
new_typeNoType of the synthesised node (defaults `summary`). A CLOSED vocabulary, one of exactly these: `episode`, `task`, `checklist`, `roadmap`, `experiment`, `hypothesis`, `scratch`, `draft`, `audit_event`, `chain`, `board`, `idea`, `outcome`, `reference`, `concept`, `entity`, `summary`.summary
initiativeNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.7.5

TDQS

A4.2/5.0
Behavior3/5

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

No annotations are provided, so the description carries the full behavioral burden. It usefully discloses that edges (derived_from) are written so `trace` can walk back, which is real graph-side-effect info. However it says nothing about whether the seed nodes are consumed/retained, whether the operation is reversible, or any permission/conflict requirements for a mutation tool.

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?

Two tight sentences with zero filler; the core operation and its graph effect are front-loaded, and the sibling contrast is placed last where it is cheapest to skip. Nothing is repeated from the schema.

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?

For a 6-parameter mutation tool with an output schema absent but 83% schema coverage, the description covers the operation, graph effect, and sibling routing adequately. It is slightly thin on the optional parameters (tier/new_type/initiative) and on seed-node lifecycle, which keeps it below a 5.

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 coverage is 83%, so the schema already documents most parameters well (including the closed tier/new_type vocabularies). The description only implicitly maps 'seeds' to `from` and 'durable node' to `new_name`/`new_body`, adding no syntax or constraint detail beyond what the schema carries. Baseline 3 is appropriate.

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?

States a specific verb+resource (write one durable node from several seeds) and adds the structural consequence (derived_from edges to each seed). It explicitly names and contrasts the sibling `settle` as the one-to-one variant, so an agent can distinguish the two without opening either schema.

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?

Gives an explicit when-to-use condition ('when scattered observations have converged into a single finding') and names the nearest alternative (`settle`) with the discriminator (one-to-one, in-place promotion). Both the trigger and the routing rule are spelled out.

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