io.github.dan24ou-cpu/agent-signal
NPM · AGENT-SIGNAL · 2 COMPONENTS · SCANNED AUG 3
Collective intelligence for AI shopping agents — product intel, deals, and more
Available components
How this component scores in each security and reliability category. Every signal is checked automatically from public evidence about the published package, including repeated runs of it in an isolated sandbox, and we only credit what we can confirm. How we score →
Supply Chain Security87
- No malware found by supply-chain analysis.Pass
- Only part of the dependency tree could be resolved (138 of 140), so this covers what we could see, not the whole tree.Partial
- No install/post-install scripts declared.Pass
- Only part of the dependency tree could be resolved (138 of 140), so this covers what we could see, not the whole tree. View diagnostics → Partial
Provenance & Transparency45
- Source repository is publicly reachable at the declared URL. View diagnostics → Pass
- Provenance check failed: no build-provenance attestation is published. See how to fix → View diagnostics → Fail
- Clear OSI-approved license (MIT).Pass
- Actively maintained (last published 122 days ago).Pass
- Disclosure check failed: no security disclosure policy was found in the source repository. See how to fix → Fail
Schema Quality & AI Usability69
- AI-judged instruction clarity (good).Pass
- Context-footprint check failed: tool/resource definitions use about 1445 tokens (~111/item across 13 items; 13 tools + 0 resources), over budget; trim descriptions and params. See how to fix → Fail
- Usage-examples check failed: none of the tools include examples. See how to fix → Fail
Stability & Change Management27
- Stability observed for 8 of 30 days with no destabilising changes; credit accrues until the full window elapses.Partial
Tool Coverage96
- 100% of tools have a non-trivial description (not blank, and not just the tool's name).Pass
- 89% of tool parameters carry a description.Partial
Capabilities100
- Implements a supported MCP spec version (2025-11-25); the latest is 2026-07-28.Pass
Add this component to your MCP client. Where a client-specific snippet is available, pick your client below and copy it straight into your config; otherwise use the connection detail shown.
npm · agent-signal
claude mcp add dan24ou-cpu-agent-signal -- npx -y agent-signal
codex mcp add dan24ou-cpu-agent-signal -- npx -y agent-signal
{
"$schema": "https://opencode.ai/config.json",
"mcp": {
"dan24ou-cpu-agent-signal": {
"type": "local",
"command": [
"npx",
"-y",
"agent-signal"
],
"enabled": true
}
}
} openclaw mcp add dan24ou-cpu-agent-signal --command npx --arg -y --arg agent-signal
mcp_servers:
dan24ou-cpu-agent-signal:
command: "npx"
args: ["-y", "agent-signal"] {
"mcpServers": {
"dan24ou-cpu-agent-signal": {
"command": "npx",
"args": [
"-y",
"agent-signal"
]
}
}
} Every change we have recorded for this component, newest first. Security-relevant changes are always shown. ▲ marks a change for the better, ▼ a change for the worse; unmarked changes are neutral.
- 3 Aug 26 +4
- Stability: unverified → 0.27 ▲ functional
- 2 Aug 26 +44
- Provenance: unverified → fail ▼ security
- Known CVEs: unverified → partial ▲ security
- Install scripts: unverified → pass ▲ security
- Malware scan: unverified → pass ▲ security
- Stability: Stability not yet verified: not enough scan history yet (needs a 30-day window). security
- Schema quality: unverified → good ▲ functional
- Maintenance: unverified → pass ▲ functional
- Dependency health: unverified → partial ▲ functional
- License: unverified → pass ▲ functional
- MCP protocol: unverified → pass ▲ functional
- Licence: MIT functional
- 31 Jul 26 −26
- We updated how we score, so this day's move reflects our rubric, not a change to the server See what changed → functional
- 28 Jul 26 +22
- Tool coverage: unverified → 100 ▲ functional
- First check of Schema quality: unverified functional
- First check of Schema quality: fail functional
- First check of Schema quality: fail functional
- First check of Tool coverage: 89 functional
- 27 Jul 26 24
First indexed and scored.
Diagnostic detail from the automated scan of this channel: what the scanner observed at each step, so you can see exactly where a check passed or failed. It is informational only and never changes the trust score.
Captured 3 Aug 2026 · Analysed npm/[email protected]
Provenance none
Ecosystem: npm · Outcome: none
Dependencies 138 packages
138 packages in the resolved dependency tree · 138 deprecated · 39 stale.
The dependency tree was only partially resolved, so these counts may be incomplete.
The tools this component advertises to a client, with an estimated token cost for each. Expand a tool to see its parameters and schema. The per-tool counts are indicative and are not scored directly; the schema's total context footprint is one signal in Schema Quality & AI Usability.
check_merchant_reliability Check Merchant Reliability ~81
Check a merchant's reliability based on data from other AI agents' shopping sessions. Returns selection rate, stock reliability, match scores, and purchase outcomes. Use this to decide whether to trust a merchant's listings before recommending their products.
| Name | Type | Req | Description |
|---|---|---|---|
| merchant_id | string | yes | Merchant identifier to look up, e.g. 'amazon', 'bestbuy' |
No output schema declared.
No examples provided.
detect_deal Detect Deal ~107
Compare a product's current price against historical price data from all agents. Returns a verdict (best_price_ever, great_deal, good_deal, fair_price, above_average), savings vs average, and which merchants typically have the best prices. Use this before recommending a purchase to flag deals or overpricing.
| Name | Type | Req | Description |
|---|---|---|---|
| current_price | number | yes | The price you're seeing right now |
| product_id | string | yes | Product identifier, e.g. 'sony-wh1000xm5' |
No output schema declared.
No examples provided.
get_category_recommendations Get Category Recommendations ~98
Get intelligence about a product category from other AI agents' shopping sessions. Returns which products agents are selecting most, what decision factors matter, common requirements, and average budgets. Use this when starting a shopping task to understand what's working well in a category.
| Name | Type | Req | Description |
|---|---|---|---|
| budget_max | number | — | Optional budget ceiling to filter recommendations |
| category | string | yes | Product category, e.g. 'footwear/running', 'electronics/headphones' |
No output schema declared.
No examples provided.
get_constraint_match Match Constraints to Products ~128
Decision shortcut: find what products worked for agents with your EXACT constraints. Matches on specific requirements (e.g. 'wide fit + cushioned + under $150') and returns what those agents selected, why, and where to buy. Also returns broader recommendations from sessions with overlapping constraints. Use this to skip the search when a proven answer already exists.
| Name | Type | Req | Description |
|---|---|---|---|
| budget_max | number | — | Maximum budget |
| category | string | yes | Product category, e.g. 'footwear/running' |
| constraints | array | yes | Required attributes, e.g. ['wide fit', 'cushioned'] |
No output schema declared.
No examples provided.
get_product_intelligence Get Product Intelligence ~87
Get crowdsourced intelligence about a product from other AI agents' shopping sessions. Returns selection rate, common rejection reasons, which competitors beat it and why, price ranges seen, and outcome data. Use this before recommending a product to understand how other agents have evaluated it.
| Name | Type | Req | Description |
|---|---|---|---|
| product_id | string | yes | Product identifier to look up, e.g. 'sony-wh1000xm5' |
No output schema declared.
No examples provided.
get_session_summary Get Session Summary ~50
Retrieve a full summary of a shopping session including all product evaluations, comparisons, and the final outcome. Useful for reviewing what happened during a session.
| Name | Type | Req | Description |
|---|---|---|---|
| session_id | string | yes | Session ID to retrieve |
No output schema declared.
No examples provided.
get_similar_session_outcomes Learn from Similar Sessions ~126
Cross-agent learning: see what other AI agents chose when shopping for similar items with similar constraints. Returns which products were selected most often, what deciding factors mattered, and how sessions ended (purchased vs abandoned). Use this at the START of a shopping task to leverage collective agent intelligence.
| Name | Type | Req | Description |
|---|---|---|---|
| budget_max | number | — | Maximum budget to filter similar sessions |
| category | string | yes | Product category, e.g. 'footwear/running' |
| constraints | array | yes | Shopping constraints, e.g. ['lightweight', 'cushioned', 'wide fit'] |
No output schema declared.
No examples provided.
get_warnings Get Warnings ~94
Check for recent problems with a product or merchant before recommending them. Surfaces stock issues, high rejection rates, and abandonment signals from other agents. Returns warnings with severity levels (critical, warning, info) or all_clear if safe. Use this as a safety check before finalizing a recommendation.
| Name | Type | Req | Description |
|---|---|---|---|
| merchant_id | string | — | Merchant to check for warnings |
| product_id | string | — | Product to check for warnings |
No output schema declared.
No examples provided.
import_completed_session Import Completed Shopping Session ~124
Bulk-import an entire shopping session after the fact. Use this if you already completed a shopping task WITHOUT agent-signal tools and want to contribute your decision data retroactively. Accepts the full session in one call: intent, product evaluations, comparisons, and outcome. This helps the collective intelligence even when agent-signal wasn't used during the original task.
| Name | Type | Req | Description |
|---|---|---|---|
| comparisons | array | — | Comparisons made between products |
| evaluations | array | — | Products that were evaluated |
| outcome | object | — | Final outcome of the session |
| session | object | yes | Shopping session intent |
No output schema declared.
No examples provided.
log_comparison Log Product Comparison ~121
Log a comparison between two or more products during a shopping session. Call this when the agent explicitly compares products to decide between them. Record which product won and what the deciding factor was.
| Name | Type | Req | Description |
|---|---|---|---|
| deciding_factor | string | yes | The primary factor that decided the winner |
| dimensions_compared | array | — | Dimensions compared, e.g. ['price', 'reviews', 'durability'] |
| products_compared | array | yes | Product IDs being compared |
| session_id | string | yes | — |
| winner_product_id | string | yes | The product that won the comparison |
No output schema declared.
No examples provided.
log_outcome Log Shopping Outcome ~85
Log the final outcome of a shopping session. Call this when the shopping task ends — whether the user purchased, received a recommendation, abandoned, or deferred the decision.
| Name | Type | Req | Description |
|---|---|---|---|
| outcome_type | string | yes | — |
| product_chosen_id | string | — | Product ID if purchased or recommended |
| reason | string | — | Why this outcome occurred |
| session_id | string | yes | — |
No output schema declared.
No examples provided.
log_product_evaluation Log Product Evaluation ~172
Log that a product was evaluated during a shopping session. Call this for each product the agent considers, whether it's selected, rejected, or shortlisted. Include the rejection reason if the product was rejected.
| Name | Type | Req | Description |
|---|---|---|---|
| disposition | string | yes | Whether the product was selected, rejected, or shortlisted |
| in_stock | boolean | — | — |
| match_reasons | array | — | Why this product was a match |
| match_score | number | — | How well the product matches intent (0-1) |
| merchant_id | string | — | Merchant/retailer identifier |
| price_at_time | number | — | Price at time of evaluation |
| product_id | string | yes | Product identifier |
| rejection_reason | string | — | Why the product was rejected |
| session_id | string | yes | Session ID from log_shopping_session |
No output schema declared.
No examples provided.
log_shopping_session Log Shopping Session ~172
Start a new shopping session by logging the user's shopping intent. Call this at the beginning of any shopping task to capture what the user wants. Returns a session_id to use with subsequent log calls.
| Name | Type | Req | Description |
|---|---|---|---|
| agent_platform | string | — | Agent platform identifier |
| budget_currency | string | — | Budget currency code |
| budget_max | number | — | Maximum budget amount |
| category | string | — | Product category, e.g. 'footwear/running' |
| constraints | array | — | Required attributes, e.g. ['wide fit', 'cushioned'] |
| exclusions | array | — | Excluded brands or features, e.g. ['Nike'] |
| gift | boolean | — | Whether this is a gift purchase |
| raw_query | string | yes | The user's original shopping request |
| urgency | string | — | — |
No output schema declared.
No examples provided.