Skip to main content
Glama

Extract Facts From Sessions

extract_facts_from_sessions

Scan the tenant's seeded sessions with rule-based extractors (money, counts, dates, project-role, acquire, version-chain) and emit structured facts to the projection stream so they become queryable via enumerate_memory_facts. Use when enumerate_memory_facts returns insufficient rows for aggregation, version-chain, or money questions and you suspect the fact exists but was under-predicated at ingest. Idempotent — safe to re-run (duplicate fact_hashes skipped unless overwrite_existing=true). Profile 'comprehensive' runs all rule families; narrower profiles ('money', 'counts', 'dates', 'version_chains') target a single family. Returns facts_added + rules_matched + receipt_id. Gated by FACT_EXTRACTION_MODE on the server.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
session_idsNo
extraction_profileNo
overwrite_existingNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

TDQS

A4.6/5.0
Behavior5/5

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

With no annotations provided, the description fully carries the behavioral disclosure burden. It reveals side effects (emitting facts to the projection stream), idempotency ('duplicate fact_hashes skipped unless overwrite_existing=true'), return values ('facts_added + rules_matched + receipt_id'), and a server-side gating condition ('FACT_EXTRACTION_MODE'). This level of transparency exceeds typical descriptions and gives the agent crucial operational context.

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 information-dense but every sentence adds value: it covers the action, use case, idempotency, profiles, return values, and gating. There is no redundancy or filler, and the key facts are front-loaded—the opening sentence immediately establishes what the tool does. This is an exemplary balance of conciseness and completeness.

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?

The description is highly complete given the tool's complexity and the existence of an output schema. It explains when to use the tool, what it returns, the extraction profiles, and idempotency. Minor gaps remain: the meaning of 'seeded sessions' and how session_ids interacts with the scan scope is unclear, and the output schema (not provided in the prompt) would handle return value specifics. Still, the description provides sufficient context for most use cases.

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 0%, so the description must compensate for parameter meaning. It does explain extraction_profile by listing its enum values and their behavior, and overwrite_existing is clarified in the idempotency note. However, the session_ids parameter is not explicitly described; the phrase 'seeded sessions' is ambiguous about whether it refers to all seeded sessions or is filtered by session_ids. Since one of three parameters lacks clear semantics, the description only partially compensates for the missing schema documentation.

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 clearly states the tool's function: 'Scan the tenant's seeded sessions with rule-based extractors' and 'emit structured facts to the projection stream so they become queryable via enumerate_memory_facts.' It specifies the resource (sessions), the action (extract facts), and the outcome (queryable facts), effectively distinguishing it from sibling tools like enumerate_memory_facts, which is referenced as the query counterpart.

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?

Explicit usage guidance is provided: 'Use when enumerate_memory_facts returns insufficient rows for aggregation, version-chain, or money questions and you suspect the fact exists but was under-predicated at ingest.' It also explains profile selection ('comprehensive' vs. narrower profiles) and emphasizes idempotency, making it safe to re-run. This clear guidance helps an agent decide when to invoke this tool versus alternatives.

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

Try in Browser

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

C2.8/5.0
Disambiguation2/5

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.

Naming Consistency4/5

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.

Tool Count1/5

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.

Completeness3/5

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.

Resources