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Glama

Server Configuration

Describes the environment variables required to run the server.

NameRequiredDescriptionDefault
TAVILY_API_KEYNoAPI key for Tavily backend to enable external web research when no corpus is provided.
AGENT_SYSTEM_EMBEDDING_MODELNoModel for sentence-transformers embedding provider.sentence-transformers/all-MiniLM-L6-v2
AGENT_SYSTEM_EMBEDDING_PROVIDERNoEmbedding provider: 'auto', 'sentence-transformers', or 'hash'.auto
AGENT_SYSTEM_EMBEDDING_CACHE_DIRNoPath to cache directory for embeddings.
AGENT_SYSTEM_EMBEDDING_LOCAL_ONLYNoSet to 'true' to use local-only embeddings, or 'false' otherwise.

Instructions

Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.

This server publishes no instructions, or was last inspected before Glama recorded them.

Capabilities

Features and capabilities supported by this server

Protocol revision2025-11-25

CapabilityDetails
tools
{
  "listChanged": false
}
prompts
{
  "listChanged": false
}
resources
{
  "subscribe": false,
  "listChanged": false
}
experimental
{}

Tools

Functions exposed to the LLM to take actions

NameDescription
search_project_memoryA

Search project memory by query string and optional type filters.

write_project_memoryC

Write a durable memory entry with provenance to the backing store.

list_related_artifactsB

List artifacts related to a run or query.

record_decisionC

Record a high-value architectural or implementation decision.

record_failure_modeA

Record a reusable failure pattern or bug signature.

get_file_contextB

Read file context from the orchestrator workspace.

checkpoint_runB

Apply a small state patch and create an explicit checkpoint.

plan_goalB

Generate a large-app execution blueprint from a high-level goal and optional JSON spec.

create_runA

Create a new orchestrator run from a high-level goal and optional JSON spec.

resume_runA

Resume an orchestrator run until completion, pause, or failure.

Prompts

Interactive templates invoked by user choice

NameDescription
research_subagent_briefTemplate for a bounded research worker
code_implementation_briefTemplate for a bounded implementation worker
review_rubricTemplate for review criteria
repair_node_briefTemplate for bounded repair work
decision_recording_templateTemplate for decision capture
large_app_planning_briefTemplate for turning a product request into a large-app execution blueprint

Resources

Contextual data attached and managed by the client

NameDescription
project_decisionsActive architectural decisions
project_interfacesActive interface and contract memory
project_known_bugsKnown reusable failure modes
project_conventionsActive patterns and conventions
planner_profilesAvailable planner profiles and intended use

TDQS

A3.5/5.0

Scored across 10 tools

Disambiguation3/5

Most tools target distinct actions or resources, but plan_goal and create_run both take a high-level goal and optional JSON spec, making their boundaries unclear. Similarly, search_project_memory and list_related_artifacts both retrieve stored information in overlapping ways. Descriptions help clarify intent, but some pairs could still lead to misselection.

Naming Consistency5/5

All tools follow a consistent verb_noun snake_case pattern, such as resume_run, write_project_memory, record_decision, and create_run. The naming is uniform and predictable across the entire toolset.

Tool Count5/5

Ten tools is a well-scoped count for an orchestrator and memory management server. Each tool covers a meaningful operation without the set feeling bloated or overly sparse.

Completeness4/5

The core lifecycle is covered: creating and resuming runs, checkpointing, planning, memory read/write, and recording decisions/failures. Minor gaps exist, such as no explicit run status/query tool or delete operation for memory entries, but agents can generally work within the provided surface.

Maintenance

ActivityInactive
ResponsivenessNo issues