Skip to main content
Glama

save-llm-config

Destructive

Save AI Visibility monitoring settings for a project, including topics, models, cadence, and prompts on initial setup.

Input Schema

NameRequiredDescriptionDefault
topicsYes
contextYesExplain in 15-25 words, in third person, why this tool is called and how it supports the user's goal. For analytics only. You MUST describe only the abstract purpose of the tool call. NEVER include, repeat, paraphrase, or infer personal, sensitive, or identifying information from the user request or tool results, including names, emails, phone numbers, IPs, IDs, or credentials. You MUST generalize specific entities into roles such as "a user", "the customer", or "an account". Example: "Retrieving a customer's recent orders to investigate a billing issue and help support determine the appropriate resolution."
promptsNo
modelIdsNo
funnelMixNo
llm_modelYesThe exact model identifier you (the assistant) are running as, taken from your system prompt or environment (e.g. "claude-opus-4-8", "gpt-5.2"). Used for analytics only. If you do not know your model identifier with certainty, pass "unknown" — never guess.
autoRunWeekdaysNo
defaultLocationNo
autoRunFrequencyYes
advertiserProjectIdYes
runFirstAnalysisNowNo
hiddenCompetitorDomainsNo
pinnedCompetitorDomainsNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

B3.1/5.0
Behavior3/5

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

Annotations declare destructiveHint=true and readOnlyHint=false, so the agent knows this mutates. The description adds useful scoping context ('initial setup') but never discloses what the destructive behavior actually is — whether it overwrites an existing configuration, replaces topics/prompts, or is irreversible. That is the most important thing to know here and it is left implicit.

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?

One front-loaded sentence with no filler; the scoping phrase 'on initial setup' is placed at the end where it belongs. It is efficient, though it is arguably too short for a 13-parameter destructive tool.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With 13 parameters at 15% description coverage, a destructive mutation hint, and no output schema, the definition needs considerably more: what the save replaces, required vs optional parameter intent, and how cadence enumerations interact with autoRunWeekdays. As written, an agent could call it correctly only by reading the schema closely.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Only 15% schema description coverage across 13 parameters, so the description carries the burden and covers just 4 concepts (topics, models, cadence, prompts). The remaining parameters — funnelMix, pinned/hiddenCompetitorDomains, defaultLocation, runFirstAnalysisNow, autoRunWeekdays, advertiserProjectId — get no mention or explanation. The description does not compensate for the low coverage.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description names a specific verb (Save) and resource (AI Visibility monitoring settings) and enumerates the facets it covers: topics, models, cadence, and prompts. It also scopes the action to 'initial setup,' which separates it from read-oriented siblings like get-llm-monitoring. It does not, however, explicitly name a sibling it is not.

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 phrase 'on initial setup' implies when this tool is appropriate, distinguishing first-time configuration from later reads or deletions. But no alternative is named and no exclusion (e.g., 'use update-llm-monitoring to change existing settings' or 'use add-llm-prompt for single prompt additions') is given, so the agent must infer.

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.