Deep enrichment: name + email + phone + LinkedIn + firmographic data + tech stack signals + funding history + revenue estimates. Multi-source fusion (5+ sources). For sales teams that need everything. Tier 2: $15-500 (small batch). Tier 3: $250-3000 (high volume).
| Name | Required | Description | Default |
|---|---|---|---|
| industry | No | ||
| quantity | Yes | ||
| tech_stack | No | Filter by tech they use (e.g., 'Shopify', 'Salesforce'). | |
| funding_stage | No | ||
| revenue_range | No |
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?
No annotations are provided, so the description must disclose behavioral traits. It mentions pricing tiers (cost) but does not state whether the tool is read-only, destructive, or has rate limits, authentication needs, or other safety characteristics. The description is insufficient for an agent to understand side effects.
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 description is concise (4 sentences) and front-loads the main offering ('Deep enrichment: ...'). Pricing info is secondary but relevant. No wasted words, though the structure could be improved by separating functional description from pricing.
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?
Given the complexity (5 parameters, no output schema), the description is incomplete. It does not explain what the tool returns beyond a list of data types, how to use parameters effectively, or ordering/filtering behavior. Without an output schema, the agent cannot understand the response format, making it inadequate for a tool of this depth.
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?
Schema description coverage is low (20%): only tech_stack has a description. The description lists data points (name, email, phone, etc.) but these are not parameters—parameters are industry, quantity, tech_stack, funding_stage, revenue_range. It does not explain industry, funding_stage, or revenue_range, nor does it add meaning beyond the schema. The description fails to compensate for missing schema documentation.
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 states this is a 'deep enrichment' tool that returns multiple data types (name, email, phone, LinkedIn, firmographic, tech stack, funding, revenue). It mentions multi-source fusion, which distinguishes it from simpler tools. However, it does not explicitly differentiate from siblings like hsh-b2b-contact or hsh-b2b-enriched, leaving some ambiguity.
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 says 'For sales teams that need everything,' implying use when comprehensive data is required. But it lacks explicit when-to-use or when-not-to-use guidance, and does not mention alternatives or exclusions. The context is clear but not directive.
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.
Most tools have distinct purposes, but some closely related tools (e.g., hsh-b2b-*, hsh-esg-* variants) could cause confusion. Descriptions help differentiate, but an agent might still misselect similar products.
Naming convention is mixed: some tools use hyphens (hsh-b2b-contact), others use underscores (hsh_broker_data_request). While mostly readable, the inconsistency could be confusing for agents expecting a uniform pattern.
32 tools is on the high side for a single server, but given its purpose as a data marketplace, the large number reflects a wide catalog. However, it may be overwhelming for agents to navigate.
Covers many data domains but has obvious gaps (e.g., weather, social media). The inclusion of custom data request tools (hsh_describe_data_need, hsh_broker_data_request) mitigates these gaps, allowing agents to request missing data.
