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Glama

Salesforce AgentForce CKG

by io.github.Yarmoluk

route_query

Route an AgentForce question to the optimal model and reasoning approach via graph depth.

The CKG graph IS the router. AgentForce dependency chains (e.g. Einstein Trust Layer → Data Cloud → NVIDIA NIM → Resolution Criteria) have typed hops that signal reasoning complexity deterministically. No heuristic: the graph decides.

Routing table: hop_depth 1 → haiku · direct (simple concept lookup) hop_depth 2 → sonnet · generic_cot (moderate chain) hop_depth 3+ → opus · sparql_cot (deep dependency, structured reasoning)

Args: question: Concept name or natural language question about Salesforce AgentForce.

Returns: model_tier + reasoning_approach + why + context subgraph to inject before LLM call.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
questionYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

A4.3/5.0
Behavior5/5

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

With no annotations, the description fully discloses the behavioral logic: deterministic routing via graph depth with a clear routing table. It explains that 'No heuristic: the graph decides' and specifies return values, offering complete transparency.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured: a concise summary, then mechanism explanation, routing table, and input/output definitions. It is slightly verbose with 'No heuristic: the graph decides,' but overall efficient and front-loaded.

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 covers purpose, input semantics, algorithmic behavior, and return structure. With an output schema present, the agent can infer return format. It is nearly complete but could mention error handling or input constraints.

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?

The description defines the single parameter 'question' as 'Concept name or natural language question about Salesforce AgentForce,' adding significant meaning beyond the schema's type definition. With 0% schema coverage, this compensates well, though examples could enhance clarity.

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 explicitly states the tool's function: 'Route an AgentForce question to the optimal model and reasoning approach via graph depth.' It uses a specific verb ('route') and resource ('AgentForce question'), and the routing table distinguishes it from siblings.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage for routing questions based on graph depth but lacks explicit guidance on when to choose this tool over siblings like 'query_ckg' or 'verify_source'. No 'when not to use' or alternative comparisons are provided.

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

A4.1/5.0
Disambiguation4/5

Most tools serve clearly distinct roles: querying, searching, routing, and verification are separate concerns. However, query_ckg and get_prerequisites both return prerequisite information, which could cause an agent to select the wrong tool when a simple prerequisite list is needed. The descriptions do clarify the difference (generic traversal vs. full ordered chain), but some ambiguity remains.

Naming Consistency4/5

The vast majority of tools follow a consistent snake_case verb_noun pattern, such as evaluate_trust_chain, list_concepts, and route_query. The only exception is resolution_path, which is a noun phrase rather than a verb_noun, making it slightly less predictable. This is a minor deviation from an otherwise strong pattern.

Tool Count5/5

With 10 tools, the server has a well-scoped surface area for a knowledge graph that requires discovery, traversal, routing, and audit capabilities. Each tool represents a distinct operation, and there are no redundant or unnecessary entries. This falls comfortably within the ideal range for a domain-specific server.

Completeness5/5

The toolset covers the full read-side lifecycle of the knowledge graph: discovery, traversal, path analysis, routing, and trust verification. It also includes specialized tools for benchmarking and source verification, which are unusual but valuable additions. No obvious critical gaps exist, such as missing search or traversal capabilities.

Resources