How the 152 servers that stated who compensates their operator actually did it - the field names observed in the wild, with counts. Not a specification we invented.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It discloses that the data is empirical ('observed in the wild'), includes counts, and is not an invented specification. This is meaningful behavioral context for a zero-parameter reference tool. It doesn't describe return formatting, but the scope is simple enough that the description adds adequate transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The entire description is one sentence that front-loads the subject and scope, adds the key detail about counts, and closes with an important caveat. Every clause earns its place; there is no filler.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a zero-parameter tool with no annotations and no output schema, the description is nearly complete: it says what data is covered, its provenance, and that it contains counts. The main missing piece is an explicit statement of what the returned data structure looks like, but the description gives enough for an agent to know what the tool offers.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters, so the baseline is 4. The description doesn't add parameter-specific detail because there are none to document. This is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly identifies the tool as an observational guide to field names used by 152 servers to disclose operator compensation, with counts. It uses specific nouns ('field names observed in the wild') and specifies the scope ('the 152 servers'). It doesn't use an action verb like 'retrieve' or 'list', and doesn't name a sibling, so it earns a 4 rather than a 5.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage: when you need to know how servers actually disclosed operator compensation, consult this guide rather than a specification. The phrase 'Not a specification we invented' implicitly warns against treating it as normative. However, it gives no explicit comparison with sibling tools like lookup, method, or summary, so it stops at implied usage.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Each tool covers a clearly separate concern: aggregate counts, per-address records, live probing, methodology, measurement metadata, and disclosure field names. The only mild overlap (counts in summary vs disclosure_guide) is disambiguated by their descriptions.
All tools share the mcp_observatory_ prefix and snake_case, but the suffixes are mostly nouns (state, summary, method, lookup, disclosure_guide) with one verb-like exception (measure_now). This is a minor deviation rather than a chaotic mix.
Six tools is well-scoped for a read-only observatory server. Each tool adds a distinct capability and none feel redundant or padding.
The surface covers the main tasks: querying recorded results, running a live probe, understanding methodology and limitations, and getting aggregate counts. The notable gap is the explicit absence of any tool listing addresses, but that can be worked around by obtaining addresses from the official registry.